AED: Adaptable Error Detection for Few-shot Imitation Policy
Authors: Jia-Fong Yeh
Kuo-Han Hung
Pang-Chi Lo
Chi Ming Chung
Tsung-Han Wu
Hung-Ting Su
Yi-Ting Chen
Winston H. Hsu
Venue: NeurIPS 2024 poster (2024)
Keywords: adaptable error detection
few-shot imitation
policy learning
Primary area: machine_vision
TL;DR: The novel adaptable error detection (AED) problem is formulated for monitoring few-shot imitation policies' behaviors, and we propose PrObe to address the challenging problem by learning from the policy's feature representations.

Abstract: We introduce a new task called Adaptable Error Detection (AED), which aims to identify behavior errors in few-shot imitation (FSI) policies based on visual observations in novel environments. The potential to cause serious damage to surrounding areas limits the application of FSI policies in real-world scenarios. Thus, a robust system is necessary to notify operators when FSI policies are inconsistent with the intent of demonstrations. This task introduces three challenges: (1) detecting behavior errors in novel environments, (2) identifying behavior errors that occur without revealing notable changes, and (3) lacking complete temporal information of the rollout due to the necessity of online detection. However, the existing benchmarks cannot support the development of AED because their tasks do not present all these challenges. To this end, we develop a cross-domain AED benchmark, consisting of 322 base and 153 novel environments. Additionally, we propose Pattern Observer (PrObe) to address these challenges. PrObe is equipped with a powerful pattern extractor and guided by novel learning objectives to parse discernible patterns in the policy feature representations of normal or error states. Through our comprehensive evaluation, PrObe demonstrates superior capability to detect errors arising from a wide range of FSI policies, consistently surpassing strong baselines. Moreover, we conduct detailed ablations and a pilot study on error correction to validate the effectiveness of the proposed architecture design and the practicality of the AED task, respectively. The AED project page can be found at https://aed-neurips.github.io/.

AED: Adaptable Error Detection for Few-shot Imitation Policy
Jia-Fong Yeh!
Kuo-Han Hung
Pang-Chi Lo Chi-Ming Chung
Tsung-Han Wu
Hung-Ting Su'
Yi-Ting Chen
Winston H. Hsu 1.4
National Taiwan University
University of California, Berkeley
3 National Yang Ming Chiao Tung University
"MobileDrive
Abstract
We introduce new task called Adaptable Error Detection (AED), which aims to identify behavior errors in few-shot imitation (FSI) policies based visual observations in novel environments. The potential to cause serious damage to surrounding areas limits the application of FSI policies in real-world scenarios. Thus, a robust system is necessary to notify operators when FSI policies are inconsistent with the intent of demonstrations, This task introduces three challenges: (I) detecting behavior errors in novel environments, (2) identifying behavior errors that without revealing notable changes, and (3) lacking complete temporal information of the rollout due to the necessity of online detection. However, the existing benchmarks cannot support the development of AED because their tasks do not present all these challenges. To this end, we develop a cross-domain AED benchmark, consisting of 322 base and 153 novel environments. Additionally, we propose Pattern Observer (PrObe) to address these challenges. PrObe is equipped with a powerful pattern extractor and guided by novel learning objectives to parse discernible patterns in the policy feature representations of normal or етгог states. Through our comprehensive evaluation, PrObe demonstrates superior capability to detect errors arising from a wide range of FSI policies, consistently surpassing strong baselines. Moreover, we conduct detailed ablations and a pilot study on етror correction to validate the effectiveness of the proposed architecture design and the practicality of the AED task, respectively. The AED project page can be found at htthtps:///eee neur wwt.pp...ttttttt. wit.gt.t.iiii. io/
1 Introduction
Few-shot imitation (FSI), a framework that learns policy in novel (unseen) environments from a few demonstrations, has recently drawn significant attention in the community 222 5 6). Notably, the framework, as exemplified by 7 8 g
2] has demonstrated its efficacy across a range of task based on just few demonstrations from their owners [ 2 However, a major barrier that still limits their ability infiltrate our everyday lives is the ability detect behavior errors in novel environments.
robotic manipulation tasks. This framework shows significant potential to adapt
We propose a challenging and crucial task called adaptable error detection (AED), aiming to monitor FSI policies from visual observations and report their behavior errors, along with the corresponding benchmark. In this work, behavior errors refer to states that deviate from the demonstrated behavior, necessitating the timely termination of the policy upon their occurrence. Unlike existing few-shot visual perception tasks [12]. failures can result significant disruptions to surrounding objects and humans in the real world. This nature often restricts real-world experiments to simple tasks. Our AED benchmark built within Coppeliasim 13 and Pyrep [14, encompassing six indoor tasks and one factory task. This comprehensive benchmark comprises 322 base and 153 new environments,
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Three Challenges AED task
O
Alarm: pick failure
W Working in novel environments (unseen objects and background)
adaptable error detector
notable changes revealing when behavior errors occur (hard to observe from adjacent frames)
2-a minor visual difference
task misunderstanding
agent tw/ FSI policy TTa
current observation
fa iilure
agent
expert
Online detection
time
0
1
future stops
(lacking complete temporal information of the rollout)
Figure 1: Our novel adaptable error detection (AED) task. To monitor the behavior of the few-shot imitation (FSI) policy Te, the adaptable error detector needs to address three challenges; (1) it works in novel environments, (2) notable changes reveal when behavior errors occur, and (3) requires online detection. These challenges make existing error detection methods infeasible.
spanning diverse domains and incorporating multiple stages. We aim to create a thorough evaluation platform for AED methodologies, ensuring their effectiveness before real-world deployment.
The AED task present three novel challenges, illustrated in Figure First, AED entails monitoring a policy's behavior in novel environments, the normal states of which are not observed during training. Second, detecting behavior errors becomes challenging, as there are no noticeable changes indicate when such errors occur. Specifically, this makes it difficult to discern either minor visual differences or misunderstandings the task through adjacent frames. Third, AED requires online detection to terminate the policy timely, lacking complete temporal information of the rollout.
Current approaches struggle to address the unique challenges posed by AED. One-class classification (OCC) methods, including one-class SVMs [15] or autoencoders [16 17 I8 [19], face difficulties in handling unseen environments. These methods are trained solely with normal samples and identify anomalies by evaluating their significant deviation from the learned distribution. However, normal states novel environments, which include unseen backgrounds and objects, are already considered for these techniques, resulting in subpar performance. Multiple instance learning (20 or patch-based methods [2] 22 may alleviate the second challenge, particularly minor visual differences, in seen environments. However, the feasibility of applying these methods to AED remains underexplored. Video few-shot anomaly detection (vFSAD) methods [2] 23 24
are unsuitable for
AED due to their dependency on full temporal observation from videos.
To this end, we introduce Pattern Observer (PrObe), a novel algorithm that extracts discriminative features from the monitored policy to identify instances of behavior errors. Specifically, PrObe designs a gating mechanism to extract task-relevant features from the monitored FSI policy to mitigate the impact of novel environment (first challenge). Then, we design an effective loss function to distill sparse pattern features, making easier observe changes observation (second challenge). Additionally, we design recurrent generator to generate pattern flow of current policy. Finally, we determine if there is behavior error by proposing novel temporal-aware contrastive objective to compare the pattern flow and demonstrations (third challenge).
We conduct thorough experiments on the proposed benchmark. Even when faced with various policy behaviors and different characteristics of strong baselines, our PrObe still achieved the highest Top I counts, average ranking, and average performance difference (with a maximum difference of up to 40%), demonstrating its superiority. Additionally, we conducted an extensive ablative study to justify the effectiveness our design choices. Furthermore, we reported additional experimental results covering timing accuracy, embedding visualization, demonstration quality, viewpoint changes, and error correction validate our claims and the practicality of our task and method.
Contributions Our work makes three significant contributions: (1) We define vital yet under- explored task called Adaptable Error Detection (AED) and develop its associated benchmark to facilitate collective exploration by the research community. (2) We introduce PrObe, novel method

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that monitors the policy's 's behavior by retrieving patterns from its feature embeddings. (3) We conduct thorough evaluations on the proposed benchmark, demonstrating the effectiveness of PrObe. surpasses several baselines and shows robustness across different policies. We anticipate that our research will serve as a key foundation for future real-world experiments in the field of FSI research.
Related Work
2.1 Few-shot Imitation (FSI)
Policy With the recent progress in meta-learning (25). the community explores the paradigm for learning policies from limited demonstrations during inference [26] 27 28 Notably, these works either assume that the agent and expert have the same configuration or explicitly learn a motion mapping between them [27]. Conversely, DC methods [I [I 29 develop a policy that behaves conditioned both on the current state and demonstrations. Furthermore, they implicitly learn the mapping between agents and experts, making fine-tuning optional. Thereby, effectively extracting knowledge from demonstrations becomes the most critical matter. In most FSI works, no environment interactions are allowed before inference. Hence, policies are usually trained by behavior cloning (BC) objectives, i.e., learning the likelihood of expert actions by giving expert observations. Recently, DCRL I trains a model using reinforcement learning (RL. objects and performs FSI tasks without interacting with novel environments.
Evaluation tasks Since humans can perform complex long-horizon tasks after watching a few demonstrations, FSI studies continuously pursue solving long-horizon horizon tasks to verify if machines can achieve the same level. A set of research applies policy on a robot arm to perform daily life tasks in the real world 27 simulation [7]. The task is usually multi-stuee stalt and composed of prinitivesskkiilsssttaes
11 2 such as a typical pick-and-place task (28] or multiple boxes stacking
77. Besides, MoMaRT 19 tackles challenging mobile robot task in kitchen scene.
Our work formulates the AED task for the safety concern of FSI policies and proposes PrObe to address it, which is valuable for extensive FSI research. Besides, we have also built challenging FSI tasks containing attributes such as scenes from different fields, realistic simulation, task distractors, and various robot behaviors
2.2 Few-shot Anomaly Detection (FSAD)
Problem setting Most existing FSAD studies deal with anomalies in images 22 30
and few tackle anomalies in videos works presume only normal data given during training [30
36 24]
Moreover, problem settings are diverse. Some
33 34 35), while others train models with normal and a few anomaly data and include unknown anomaly classes during inference [3] 36
Method summary Studies that only normal training samples usually develop reconstruction- based model with auxiliary objectives, such as meta- meaanne [34], optical flow [35], and adversarial training 33 33). Besides, based methods [3] [2 reveal the performance superiority main image FSAD benchmark (37) since the anomaly are tiny defeats. Regarding video few-shot anomaly detection (vFSAD), existing works access complete video compute the temporal information for determining if it contains anomalies. In addition, vFSAD benchmarks 38 39
provide frame-level
labels to evaluate the accuracy of locating anomalies in videos.
Comparison between vFSAD and AED task Although both the vFSAD and our AED task require methods to perform in unseen environments, there are differences: (1) The anomalies in vFSAD involve common objects, while AED methods monitor the policy's behavior errors. (2) An anomaly in vFSAD results in notable change the perception field, such as a cyclist suddenly appearing on the sidewalk B8 However, notable change is evident when a behavior eπror occurs in AED. (3) The whole video is accessible in vFSAD 21 23 24, allowing for the leverage of its statistical information. In contrast, AED requires online detection to terminate the policy timely, lacking the complete temporal information of the rollout. These differences make our AED task more challenging and also render VFSAD methods infeasible.
3

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3 Preliminaries
Few-shot imitation (FST) FSI a framework worthy of attention, accelerating the development of various robot applications. Following [II] FSI task is associated with a set of base environments E and novel environments E", In each novel environment e" E", a few demonstrations D are given. The objective is to seek policy that achieves the best performance (e.g., success rate) in novel environments leveraging a few demonstrations. Note that, a task base and novel environments are semantically similar, but their backgrounds and interactive objects are disjoint. The framework takes as imput N demonstrations (collected by a RGB-D camera) and an RGB-D image of the current observation, following the setting of [TO M.I. Addressing FSI tasks typically involves three challenges in practice [1]: (1) The task is long-horizon and multi-stage. (2) The demonstrations length-variant, making each step misaligned, and (3) The expert and agent have distinct appearance or configuration. Developing a policy to solve the FSI task and simultaneously tackle these challenges is crucial.
tttttttrrriioooniiiooitin (DC) policy As stated above, the expert and agent usually have different appearances or configurations. The DC policy Tia | s, D) learns an implicit mapping using current states and demonstrations D to compute agent actions a. Next, we present the unified architecture of DC policies and how they produce the action. When the observations o and demonstrations are RGB-D images that only provide partial information, we assume that the current history h, == (0), 02, .... 0,) and demonstrations DD are adopted as inputs.
A DC policy comprises a feature encoder, a task-embedding network, and an actor. After receiving the rollout history h, the feature encoder extracts the history features fh- Meanwhile, the feature encoder also extracts the demonstration features. Then, the lask-enbed network computes the task-embedding fc to retrieve task guidance. Notably, the lengths of agent rollout and demonstrations can vary. The task-embee mosk-embedding network is expected to handle length-varian sequences by padding frames to a prefixed length or applying attention mechanisms. Afterward, the actor predicts the action for the latest observation, conditioned on the history features fr and task-embedding fg. Additionally, an optional inverse dynamics module predicts the action between consecutive observations to improve the policy's understanding of how actions affect environmental changes. At last, the predicted actions are optimized by the negative log-likelihood or regression objectives (MSE).
4 Adaptable Error Detection (AED)
Our AED task is formulated monitor and report FSI policies' behavior errors, i.e., states of policy rollouts that are inconsistent with demonstrations. The challenges posed by the AED task have been presented in Figure ! We formally state the task and describe the protocol we utilized below.
Task statement Let e denote the category of agent's behavior status, where E C,C (normal, error). When the agent with the trained FSI policy mo performs in novel environment e. an adaptable error detector ái can access the agent's rollout history h and few expert demonstrations D". It then predicts the categorical probability ÿ of the behavior status for the latest state in the history & by ohh, D P(e| enc(h, D")), where enc denotes the feature encoder, and it may be encs (o contains its own encoder) or enc,, (based on policy's 's encoder). Next, let y represent the ground truth probability, evaluate via the expectation of detection accuracy over agent rollouts X" in all novel environments E":
E Ege Sen,ma ( (1).,n))Enxx A(j.
(I)
where A(.,)) is the accuracy function that returns 1 if ø is consistent with and 0 otherwise. However, frame-Ievel labels are often lacking in novel environments due the established assumption [ that we have less control over them. Therefore, we employ a sequence-level A(:--) in our experiments.
Protocol We explain the utilized protocol (shown in Figure 2) with a practical scenario: A company develops home robot assistant. This robot can perform set of everyday missions in customers' houses (novel environments F²"), given few demonstrations Dr from the customer. Before selling, the robot is trained in the base environments E built by the company. In this scenario, both the

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Step policy training
Step AED training
Step AED inference
extract features (aptonall
| Policy TT perform FSI tasks
|
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Policy 1Tg
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AED
base environments F
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novel environments
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expert executing
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Figure 2: Our AED protocol. the successful agent rollouts X Xuuce failed agent rollouts X fail and few expert demonstrations D are available for all base environments E Then, the task contains three phases: policy training, AED training, and AED inference. We aim train an adaptable error detector @ to report policy 's behavior errors when performing in novel environments E
agent's and expert's optimal policies n are available during training, with an established assumption from the FSI society [8] [10 [1] that base environments are highly controllable. Then, we can collect successful agent rollouts X succe and few expert demonstrations D for base environments Next, we also collect failed agent rollouts X fast by intervening in the agent's x² at critical timing (e.g., the moment to grasp objects) so that X fail can possess precise frame-level error labels.
With these resources, our utilized AED protocol consists of three phases: (1) The policy कe is optimized using successful agent rollouts Xb suce and a few demonstrations D (2) The adaptable error detector is trained on agent rollouts Xuucet X)asi
fast and a few demonstrations D Besides, the detector 6 may use features extracted from policy Te encoder, whose parameters are not updated in this phase. (3) The policy Te solves the task leveraging few demonstrations D and the detector monitors the policy's behavior simultaneously. Notably, no agent rollouts are collected in this phase. Only few demonstrations D are available since the agent is in novel environments F
5 Pattern Observer (PrObe)
To address the AED task, we develop rollout augmentation approach and tailored AED method. The rollout augmentation aims to increase the diversity of collected rollouts and prevent AED methods from being overly sensitive to subtle differences in rollouts. Regarding the AED method, our insight is to detect behavior errors from policy features that contain task related knowledge, rather than independently training encoder to judge from visual observations alone. Thus, we propose Pattern Observer (PrObe), which discovers the unexpressed patterns the policy features extracted from frames of successful or failed states. Even if the visual inputs vary during inference, PrObe leverages the pattern flow and a consistency comparison to effectively alleviate the challenges posed by the AED task.
Overview
5.1 Rollout Augmentation
To ensure a balanced number of successful and failed rollouts (i.e., Xb and x)aa)) along with precise frame-level labels, we gather them using the agent's 's optimal policy ** (with intervention). However, even if the policy He trained on X it inevitably diverges from the agent's optimal policy x due to limited rollout diversity. Therefore, AED methods trained solely on the raw collected agent rollouts will be too sensitive to any subtle differences in the trained policy's rollouts, leading to high false positive rates.
'The successful agent rollouts not expert demonstrations since they might have different configurations.
5

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Eppt
pattern
history embeediings ft
pattern fp
pattern extractor
now generator [LSTM]
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Figure 3: Architecture of PrObe. PrOhe detects behavior errors through the pattern extracted from policy features. The learnable gated pattern extractor and flow generator (LSTM) compute the pattern flow of history features fn- Then, the fusion with transformed task-embeddings fc aims to compare the task consistency. PrObe predicts the behavior error based on the fused embeddings. Objectives, Lpat. Ltem, and Lots. optimize the corresponding outputs.
Accordingly, we augment agent rollouts so as to increase their diversity and dilute the behavior discrepancy between trained my and agent's x² Specifically, we iterate through each frame and its label from sampled agent rollouts and randomly apply the following operations: keep, drop, swap, and copy, with probabilities of 0.3, 0.3, 0.2, and 0.2, respectively. This process injects distinct behaviors into the collected rollouts, such as speeding (interval frames dropped), slowing down (repetitive frames), and non-smooth movements (swapped frames). We demonstrate that this approach can contribute to AED methods, as shown in Figure in the Appendix.
5.2 PrObe Architecture
As depicted in Figure 3 PrObe comprises three major components: pattern extractor, a pattern flow generator, and an embedding fusion. First, the pattern extractor take as input the history features fr. from the trained policy aiming to retrieve discriminative information from each embedding of fr- Precisely, the pattern extractor transforms fr. into unit embeddings through division by its L2 norm, thus mitigating biases caused by changes in visual inputs (first challenge). Then, learnable gate composed of a linear layer with a sigmoid function determines the importance of each embedding cell. A Hadamard product between the sign of unit embeddings and the importance scores, followed by instance normalization (IN), is applied to obtain the pattern features fp for each timestep.
Second, PrObe feeds fy into an LSTM (flow generator, third challenge) to generate the pattern flow. Intuitively, the changes the pattern flow of successful and failed rollouts will differ. On the other hand, the task-embeddings f& extracted from are transformed by an IN layer, a linear layer (FC), and tanh function, mapping the eeeeeddbedd into a space similar the pattern flow.
Third, a fusion process concatenates the pattern flow and transformed task-embeddings to compute the error predictions ý. This process is expected compare the consistency between the agent rollout and demonstrations and uses this as a basis for determining whether behavior errors occur.
Objective Functions PrObe optimized by one supervised and two unsupervised objectives. Firstly, binary cross-entropy objective Tatss is employed to optimize the error prediction ú. as frame-level labels y are available during training. Secondly, the LI loss Lpat is applied to the pattern features fp. encouraging the pattern extractor to generate sparse pattern features, facilitating the observation of pattern changes (second challenge). Thirdly, a contrastive objective Ltem. a temporal- aware variant of triplet loss [40], is developed and applied to the logit embeddings to emphasize the difference between the normal and error states in failed rollouts Xian (third challenge). The idea behind Teem is that adjacent frames' logits contain similar signals due to the temporal information from the pattern flow, even in the presence of errors (cf. Figure in the Appendix). Blindly pushing

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the logits of normal and error states far apart could mislead the model and have adverse effects. Therefore, Ltem considers the temporal relationship between samples and calculated follows:
Luem
max [logit; - logit 2 |logit - logit," ll2 + m,. 0).
N
000
Here, N is the number of sampled pairs, and the temporal-aware margin my m (1.0 a (dap dan)) adjusts based on the temporal distance of anchor, positive and negative samples. m represents the margin in the original triplet loss, while dap- dan are the clipped temporal distances between the anchor and positive sample, and the anchor and negative sample, respectively. Ultimately, PrObe is optimized through a weighted combination of these three objectives.
Experiments
Our experiments seek to address the following questions: (1) Is it better to solve the AED task by analyzing discernible patterns policy features rather than using independent encoders extract features from visual observations? (2) How do our architecture designs contribute, and how do they perform in various practical situations? (3) Can our AED task be effectively integrated with error correction tasks to provide greater overall contribution?
6.1 Experimental Settings
cannot be used evaluate the AED task because they do not effectively represent all challenges posed by the AED task, such as cross-environment training and deployment. Therefore, have designed seven cross-domain and multi-stage robot manipulation tasks that encompass both the challenges encountered in FSI [1]] and AED task. Detailed task information is provided in Section the Appendix, including mission descriptions, schematics, configurations, and possible behavioral errors. Our tasks are developed using Coppeliasim
Evaluation tasks
Existing manipulation benchmarks 41
43 44
[3] with Pyrep [14] serving as the coding interface.
FSI policies To assess the ability of AED methods to handle various policy behaviors, we implement (DC) policies to perform FSI tasks, following the descriptions in Section S and utilizing the same feature extractor and actor architecture. The primary difference among these policies lies in how they extract task embeddings from expert demonstrations, NaiveDC concatenates the first and last frames of demonstrations and averages their embeddings to employs cross-demonstration attention and fuses them at the time dimension; SCAN 11 computes tasks-embeddings using stage-conscious attention to identify critical frames in demonstrations, More details can be found in Section A of the Appendix.
three
8 2
obtain task-embeddings: DCT [
Baselines In our newly proposed AED task, we compare PrObe with four strong baselines, each possessing different characteristics, detailed in Section of the Appendix. Unlike PrObe, all baselines incorporate their own encoder to distinguish errors. We describe their strategies for detecting errors here: (1) SVDDED: A deep one-class SVM [16] determines whether the current observation deviates from the centroid of demonstration frames (single frame, 0OD). (2) TaskEmbED, model [ distinguishes the consistency between the current observation and demonstrations (single frame, metric-learning). (3) LSTMED: A deep LSTM 45 predicts errors solely based on current rollout history (temporal). (4) DCTED: A deep transformer (1 distinguishes the consistency between the current rollout history and demonstrations (temporal, metric-learning).
Metrics We report the area under the receiver operating characteristic (AUROC) and the area under the precision-rec recalll curve (AUPRC), two conventional pirehhld-indepenee
metrics in er- ror/anomaly detection literature 46 47). To compute these scores, linearly select 5000 thresholds spaced from to 1 (or SVDDED's ' outputs) and apply sequence-level accuracy function (cf. Section in the Appendix). Furthermore, conduct evaluation verify if AED methods can identify
behavior errors timely, as depicted in Figure 5
FaskFmm + NaiveDC, avoiding confusion w/ AED baselines.
!DCRL- DCT (transformer), since don't use RL training.
7

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SVDDED
TaskEmbED
LSTMEID
OCTED
PrObe
SVDDED
TaskEmbED
LSTMED
DCTED
PrObe
Clase Drawer
NoiveDC
(91.11M)
0.7404
0.6840
0.8395
0.7700
0.8186
0.7813
0.8250
0.9133
0.8350
Press Button
NaiveoC
(81 94%)
0.6956
D.4113
0.7280
0.4872
0.7051
0.3769
0.7194
0.7957
0.8851
DCT
0.8378
0.7081
0.6580
0.5413
0.7680
0.5710
0.5429
0.4886
0.7306
0.7506
(80.50%)
0.7039
0,9995
0.6493
0.6739
0,7438
(80 50%)
0.3789
0.3597
0.4468
0.5734
0.7474
SCAN
0.4079
0,6498
0.7867
0.7218
0.8978
SCAN
0:4280
0.4754
0.4086
0.7491
0.7782
(10.3399)
0.1411
0.2220
0.6677
0.6390
0.5829
(25,50%)
0.4653
0.4657
0.4487
0.6757
0.7505
SVDDED
TaskEmbED
LSTMED
DCTED
PrObe
SVDDED
TaskEmbED
LSTMED
DCTED
PrObe
Pick Place
NeiveDC
0.7074
0.6810
0.7116
0.7498
0.7635
Move Glass Cup
NaiveDC
D.4772
0.7907
0.7152
0.6884
0.8684
(30.0099)
0.7971
0/8028
0.8148
0.8041
0.8665
(42.299))
0.7837
0.9368
0.8061
0.9078
0.9605
DCT
0.6125
0.7523
0.6555
0.7745
0.8173
DCT
0.5515
0.7975
0.7354
0.0635
0.8342
(84.0515)
0.7001
0.8173
0.7964
0.8264
0.8780
(88 (88.006)
0.3191
0.6968
0.5314
0.3608
0.7961
SCAN
0.5646
0.6361
0,7050
0.7462
0.8012
0.4749
0.7079
0,7880
0.6548
0.8045
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0.5078
0.6085
0.7108
0.7063
0.8072
(582256)
0.6232
0.8820
0.8704
0.7354
0.8980
SVDDED
TaakEmbeD
LSTMED
OCTED
PrObe
SVDDED
TaskEmbeD
LSTMED
OCTED
PrObe
NaiveDC (12.20%)
0,2581
0.8078
0.5946
0,5844
0,5808
Back to Box
NaiveDC 83%)
0.4621
0.3221
0.4148
0.6022
0.8911
0.9786
0,9611
0,9452
0,9652
0.9367
0.9375
0.9665
0.9679
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Organize
DCT (78.0099)
0.5622
0.6382
0.7001
0.8702
0.8550
DCT
0.5537
0.7220
0.7707
0.4772
0.7411
0.4800
0.3922
0.6352
0.5515
0.5905
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0.8652
0.9014
0.9546
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SCAN (06 (09)
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0.6193
0.6784
0.5962
0.6759
0.5498
0.7041
0.8237
0.7489
0.8614
0.5086
0.4865
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0.6983
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SVDDED
TaskEmbED
LSTMED
DCTED
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Faclory Packing
NeiveDC
0.3338
0.5584
0.6471
0.5361
0.6635
Top
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0.5593
0.7057
0.7887
0.6772
0.7676
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DCT
0:3949
0,7201
0,7934
0.7509
0.7670
Aug.
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0.1800
0.5002
0.6325
0.6224
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4.6
2.7
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1.2
SCAN
0.6151
0.6916
0.7836
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0.8256
Aug. offfrrence
7.05%
41.63%
41.59%
44 44.20%
67.29%
(63.7594)
0.5657
0.7287
0 0,3063
0.6306
0.8622
2.81%
35.42%
81.67%
57.13%
78.03%
Figure 4: Performance comparison of AED methods on seven challenging FSI tasks. The values under each policy indicate its success rate for each task. AUROC[T] and AUPRC[T] scores are listed in the upper and lower rows for each policy, respectively, ranging from 0 to 1. According to the statistics table, PrObe achieves the highest Top counts (15 and 17 out of 21 cases), average ranking, and average performance difference in both metrics, demonstrating its superiority and robustness.
6.2 Analysis of Experimental Results
Main experiment detecting policies' behavior errors Our main experiment aims to verify whether discerning policies' behavior errors from their features is effective strategy. We follow the protocol in Figure 2 to conduct the experiment. Specifically, the FSI policies perform all seven tasks, and their rollouts are collected. Then, the AED methods infer the error predictions for these rollouts. Lastly, we report two metrics based on their inference results. Notably, the results of policy rollout are often biased (e.g., many successes failures). Thus, AUPRC provides more meaningful insights in this experiment because it is less affected by the class imbalance of outcomes.
Due to the diverse nature of policy behaviors and the varying characteristics of baselines, achieving consistently superior results across all tasks is highly challenging. Nonetheless, according to Figure 4 our PrObe achieved the highest Top I counts, average ranking, and average performance difference in both metrics, indicating PrObe's ' superiority among AED methods and its robustness across different policies. We attribute this to PrObe's design, which effectively addresses the challenges in the AED task. The average performance difference is the average performance gain compared to the worst method across cases, calculated as (score- worst_score) http://otttseeeeeee _cooe..
Additionally, we investigated cases where PrObe exhibited suboptimal performance, which can be grouped into two types: (1) Errors occurring at specific timings (e.g., at the end of the rollout) and identifiable without reference to the demonstrations (e.g., DCT policy in Organize Table). (2) Errors that are less obvious compared to the demonstrations, such as when the drawer is closed with a small gap (SCAN and DCT policies in Close Drawer). In these cases, the pattern flow does not change enough to be recognized, resulting in suboptimal results for PrObe.
8

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Successful rollout case
Failed rollout case
1.20
05
1.00
05
Probsbility
0.75
SUDDES outpults
Probability
SVODED outguits
0.20
0.00
0.0
10
timestop
timestep
label
SVDDED
TaskEmbED
LSTMED
DCTED
PrObe (Ours)
Figure 5: Visualization of timing accuracy. Raw probabilities and SVDDED outputs of selected successful (left) and failed (right) rollouts drawn. PrObe raises the the accurate timing in the failed rollout and stably recognizes normal states in the successful case.
rmll states
error states
Success rohouu
eioor occurred
bogin (dark)
failed rolouut
(brigh)
rollout progress
MbED
LSTMED
(ours)
Figure 6: t-SNE visualization of learned embeddings (representations). The green and red circles represent normal and error states, respectively. The brightness of the circle indicates the rollout progress (from dark bright). The embeddings learned by our PrObe have better interpretability because they exhibit task progress and homogeneous state clustering characteristics.
Timing accuracy Our main experiment examines whether AED methods can accurately identify behavioral errors when provided with complete policy rollouts. To further validate the timing of their predictions, we annotated a subset of rollouts (specifically the SCAN policy in Pick & Place) and visualized the raw outputs of all AED methods. SVDDED outputs the embedding distance between observation and task easeedine,, while others compute probabilities, as depicted in Figure
In the successful rollout case, both PrObe and LSTMED consistently identify states normal, while other methods misidentify them, increasing the probability that inputs represent error states. Conversely, in the failed rollout case, PrObe detects cantly elevating the probability. We attribute this short delay to the time it takes for pattern flow to induce sufficient change. Nonetheless, PrObe detects errors with the closest timing; other methods either raise probabilities too early or too late. We emphasize that this phenomenon is common and a deliberate choice.
after brief delay (< frames), signifi-
Embedding visualization To analyze whether the learned embeddings possess discernible patterns, initially extract the same 20 rollouts from the annotated dataset above using three AED methods to obtain the embeddings. Subsequently, we present the t-SNE transform [48] on these embeddings in Figure 6 Obviously, the embeddings learned by TaskEmbED and LSTMED are scattered and lack an explainable structure. In contrast, our PrObe's 's embeddings exhibit characteristics of task progress and homogeneous state clustering, i.e., the states with similar properties (e.g.. the beginnings of rollouts, the same type of failures) are closer. This experiment supports the hypothesis that PrObe can learn implicit patterns from the policy's 's feature embeddings.
Ablations and supportive experiments In response to the second question, we summarize compre- hensive ablations in the Appendix. First, Figure [1] indicates that the proposed rollout augmentation (RA) strategy increases the rollout diversity and benefits AED methods with temporal information. Second, Figure 12] demonstrates that PrObe's design effectively improves performance. Third, Figure 13 illustrates PrObe's performance stability by executing multiple experiment runs on subset of tasks and computing the performance variance. Fourth, we examine how AED methods' performance is influenced when receiving demonstrations with distinct qualities, presented Lastly, we study the influence of viewpoint changes Table g Please refer to the corresponding paragraphs in the Appendix for exhaustive versions.
Table 7

---

Table I: Error correction results. Success Rate (SR) [1] is reported. The detection threshold is set as 0.9 for all methods. The values indicate the performance of the DCT policy without correction (first column) and its collaboration with the correction policy and four AED methods (remaining columns). PrObe the only method that improves the performance, as detects the error most accurately.
DCT policy
w/ TaskEmbED
w/ LSTMED
w/ DCTED
w/ PrObe
80.56
11.65%
80.56 + 11.65% 80.28 + 11.60% 71.67 1 20.75%
82.22 1 10.17%
6.3 Pilot Study on Error Correction
To further examine the practicality of our AED task (the third question), we conducted pilot study on error correction. this experiment, the FSI policy is paused after the AED methods detect an error. Then, a correction policy from [9]. which resets the agent predefined safe pose, is applied. Finally, the FSI policy continues to complete the task. We conducted the study on the Press Button task, where errors are most likely to occur and be corrected. Besides, the correction policy is defined as moving to the top center of the table. We allowed the DCT policy to cooperate with the correction policy and four AED methods (SVDDED excluded), summarized in Table [
We have two following findings: (1) Our PrObe is verified to be the most accurate method once again. In contrast, other AED methods may cause errors at the wrong timing, making it challenging for the correction policy to improve performance (it may even have a negative impact on original successful trials). (2) The performance gain from the correction policy is minor, it lacks precise guidance in unseen environments.
We believe that error correction in novel environments warrants separate study due to its challenging nature, as observed in the pilot study. One potential solution the human-in-the-loop correction, which operates through instructions [49] ability and cost when applying to our AED task need further discussion and verification. We will leave this topic for our future work.
physical guidance (51). However, their generalization
Conclusion
We point out the importance of monitoring policy behavior errors to accelerate the development of FSI research. To this end, formulate the novel adaptable error detection (AED) task, whose three challenges make previous error detection methods infeasible. To address AED, we propose the novel Pattern Observer (PrObe) by detecting errors in the space of policy features. With the extracted discernible patterns and additional task-related knowledge, PrObe effectively alleviates AED's challenges. It achieves the best performance, confirmed by both our primary experiment and thorough ablation analyses. We also demonstrate PrObe's robustness in the timing accuracy experiment and the learned embedding visualization. Additionally, we provide a pilot study on стror correction, revealing the practicality of the AED task. Our work is an essential cornerstone in experiments.
developing future FSI research to conduct complex
Limitations and future work We carefully discuss the limitations of our work in Section F of the Appendix, including online AED inference and real-world experiments. For future work, developing unitied AED method applicable various tasks and policies is worth exploring. Additionally, discovering previously unseen erroneous behaviors remains an interesting and challenging avenue.
Acknowledgements
This work was supported part by the National Science and Technology Council, Taiwan, under Grant NSTC 1 112-2634-F-002-006 We are grateful to MobileDrive and the National Center for High- Performance Computing. We also thank reviewers and area chairs for their valuable comments and positive recognition of our work during the review process.
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Appendix
This appendix presents more details of our work, following similar flow to the main paper. First, Section A introduces the few-shot imitation (FSI) policies. Then, our PrObe's ' details are provided in Section B Besides, the strong AED baselines are depicted in Section C Next, the designed multi-stage FSI tasks are exhaustively described in Section D including task configurations, design motivations, and possible behavior errors. In addition, more experimental results shown in Section to support our claims. At last, we responsibly present our work's 's limitations in Section
A
FSI Policy Details
We follow the main paper's Section to implement three state-of- of-the-art cntnsstratioooonnntiionn (DC) policies, including NaiveDC gI. DC and IC] and monitor their behaviors.
Table 2: General components" settings of FSI policies
Visual head
input shape architecture out dim. of resnet18 out dim. of visual head dropout rate
T x 4 x 120 x 160
resnet18 18 with FC layer and dropout layer 512
256 0.5
Actor
input dim. architecture hidden dims. of MLP out dim. of pos layer dim. control layer
512 or 768
MLP with two parallel FC layers (pos & control)
[512 or 768, 256, 64)
3
Architecture settings The same visual head and actor architecture are employed for all policies to conduct fair comparison. Table and head and actor) and respective eusk-embedding network settings for better reproducibility.
and Table present the settings of general components (visual
In the visual head, we added a fully-connected and a dropout layer after a single ResNet18 to mitigate overfitting of the data from base environments. Moreover, the input dimension of the actor module is determined by adding the dimensions of the outputs from the visual head and the task-embedding network. For the DCT and SCAN policies, the task-embedding's dimension matches the visual head's output. However, the task-embedding'ss 's dimension in NaiveDC policy is twice the size.
Regarding the task-embedding network, NaiveDC concatenates the first and last frames of each demonstration Subsequently, it computes the average as the task-embeddingg without involving any attention mechanism: DCT incorporates coos-eemonsttrttion attention to fuse demonstration features, followed by applying utilizes attention mechanism to attend to each demonstration from the rollout, aiming filter uninformative frames. It then fuses attended frames to obtain the final task-embedding. Furthermore, we employ a standard LSTM in our implementation, differing from the setting described in (Р]
attention to compute the task-embedding. SCAN
Training details optimize the policies, we utilize a RMSProp optimizer 52 with a learning rate of le-4 and L2 regularizer with weight of 1e-2. Each training epoch involves iterating through all base environments. Within each iteration, we sample five demonstrations and ten rollouts from the sampled base environment. The total number of epochs varies depending on the specific tasks and is All policy experiments are conducted on Ubuntu 20.04 machine equipped with an Intel 19-9900K CPU, 64GB RAM, and NVIDIA RTX 3090 24GB GPU.
specified in Table 5
B PrObe Details
propose the Pattern Observer (PrObe) to address the novel AED task by detecting behavior errors from the policy's 's feature representations, which offers two advantages: (1) PrObe has better
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Table 3; Task-embedding network settings of FSI policies
NaiveDC
input data
demonstrations
process
concat the first and last demonstration frames and average
out dim.
512
DCT (Transformer-based)
input data
current rollout and demonstrations
process
cross-demo attention followed by rollout-demo attention
number of encoder layers
number decoder layers
number of heads normalization eps dropout rate out dim.
8
le-5
0.1
256
SCAN
input data process
current rollout and demonstrations
rollout-demo attentions for each demonstration and then average
number of LSTM layer
bi-directional LSTM
False 256
out dim.
understanding of the policy because the trained policy remains the same during both AED training and testing. This consistency enhances its effectiveness in identifying errors. (2) The additional task knowledge from the policy encoder aids in enhancing the error detection performance. We have described PrObe's architecture and their usages in Section of the main paper. Here, introduce more PrObe's design justification and training objectives.
PrObe's design justification The pattern extractor aims to extract observable patterns from em- beddings in policy features. One potential approach leverage discrete encoding, used in (53). However, the required dimension of discrete embedding may vary and be sensitive to the evaluation tasks. To address variability, leverage an alternative approach that operates in a continuous but sparse space. Additionally, the goal of the pattern flow generator is to capture the temporal information of current patterns. Due to the characteristics of the AED task, standard LSTM model is better suited than modern transformer. This is because policy rollouts are collected sequentially, and adjacent frames contain crucial information. especially when behavior errors occur. Finally, PrObe detects inconsistencies by comparing the pattern flow with the transformed task embedding. The contributions of PrObe's components are verified and shown in Figure [2 The results demonstrate that our designed components significantly improve performance.
Objectives We leverage three objectives to guide our PrObe: BCE loss Lete for error classification, a 1.1 loss T.pat for pattern enhancement, and novel temporal-aware triplet loss Tetem for logit discernibility. First, the PrObe's error prediction ý can be optimized by I.ds since the ground-truth frame-level labels y are accessible during training, which is calculated by.
N₂
Tk
Lats = N,
In + (1 - Bi.5) In(1 1),))),
Th
i=
where N, and Ta the number and length of rollouts, respectively. Then, we leverage the L1 objective Lpat to encourage the pattern extractor to learn a more sparse pattern embedding fy, which can be formulated by
1 N₂ Ta
Iepat
Spasl-
N, Tı
1=0 1=0
15

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Relationship between anchor and positive/negative sample
Case soft contras tive high task consistency low temporal connection
posiiee
anchor
negalive
Case 2.
standard contrastive high consistency tompo connection
posilive
anchor
negalive
Case hard contrastive tive consistency high tempora
posilive
negative
Figure 7: Schematic of novel temporal-aware triplet loss Item- When the anchor and positive sample are more related in both the time and task aspects, the temporal-awe margin my will be larger (the closer the two embeddings encouraged). Besides, my provides slight encouragement in case the positive sample and anchor are far away on temporal distance.
With the objective Lpat, pattern embeddings are expected to sparse and easily observable of changes, benefiting the model in distinguishing behavior errors.
Next, highlight that the time relationship should also be considered when applying contrastive learning to lemporal-sequence data, in addition to determining whether the frames are normal or errors for the task. Thus, Ltem a novel temporal-aware triplet loss [40), and the temporal-aware margin my in Ltem will have different effects depending on the relationship between the anchor and the positive/negative sample, as depicted in Figure The Ltem can be calculated by:
Liem Σ max [logit, logit,
||ogii logit," ||2 + me, 0),
(5)
where N is the number of sampled pairs, and the temporal-awar aware margin m. = m (1.0 a (dup - dan)) will be enlarged or reduced considering the temporal distance of anchor, positive and negative samples. Among them, m is the margin the original triplct loss, and dap- dan are the clipped temporal distances between the anchor and positive sample and the anchor and negative sample, respectively. With Liem, PrObe can efficiently perform contrastive learning without getting confused by blindly pulling temporally distant anchors and positive samples closer.
Lastly, the total loss Liotat combination three objectives:
Leatal = Appo + Xtem Litem + deta Ledss
(6)
where Apnes Arams and Apot are weights to balance different objectives.
Table 4: Attributes of error detection methods
policy dependent
training rollouts
temporal information
# of
method name
DC-based
output type
parameters
SVDDED
only successful both
distance
1
TaskEmbED
probability
11.52M
LSTMED
both
probability
11.85M
DCTED PrOhe (ours)
both
probability
14.49M
both
probability
0.65M
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C Error Detector Details
We compare our PrObe with several strong baselines that possesses different attributes, aiming to verify their effectiveness addressing the adaptable error detection (AED) task. In this section, comprehensively present the attributes and training details associated with these baselines.
Baseline attributes Table presents the attributes of AED baselines and our PrObe. All baselines have their encoder and are independent of the policies, which offers the advantage that they only need to be trained once and can be used for various policies. However, as result, they recognize errors only based on visual information. Now we describe their details:
SVDDED: modified deep one-class SVM method [T6] trained only with successful rollouts. It relies on measuring the distance between rollout embeddings to the center embedding to detect behavior errors, without considering the temporal information. We compute the center embedding by averaging demonstration features and minimize the embedding distance between rollout features and the center during training.
TaskEmbED: A single-frame baseline trained with successful and failed rollouts, which is a modification from the NaiveDC policy [8]. It concatenates and averages the first and last demonstration frames as the task- eusk-embedding. Then, it predicts the behavior errors conditioned on the current frame and task-embedding.
LSTMED: A deep LSTM model 45
predicts errors based solely on the current rollout. It is trained with successful and failed rollouts without access demonstration data. It is expected to excel in identifying errors that occur at similar timings to those observed during training. However, may struggle to recognize errors that deviate from the demonstrations.
DCTED: baseline with temporal information derived from the DCT policy (. It is trained with successful and failed rollouts and incorporates with demonstration information. One notable distinction between DCTED and our PrObe lies in their policy dependencies. DCTED detects emrors using its encoder, which solely leverages visual information obtained within the novel environment, Conversely, PrObe learns within the policy feature space, incorporating additional task-related knowledge.
Training details Similar to optimizing the policies, utilize RMSProp optimizer with learning rate of lc-4 and a weight regularizer with a weight le-2 to train the eиrоr detection models. During each iteration within an epoch, sample five demonstrations, failed rollouts from the sampled base environment for all error detection models except SVDDED, For SVDDED, we sample five demonstrations and twenty successful agent rollouts since it is trained solely on normal samples. Notably, all error detection experiments are conducted on the same machine the policy experiments, ensuring consistency between the two of experiments.
successful agent rollouts, and ten
D Evaluation Tasks
To obtain accurate frame-level error labels in the base environments and to create a simulation envi- ronment that closely resembles real-world scenarios, we determined that existing robot manipulation benchmarks/tasks [54] 41 42 [55
[1 43 44] did not meet our requirements. Consequently, seven challenging FSI tasks containing 322 base and 153 novel environments are developed. Their general attributes are introduced in Section D. and Table the detailed task descriptions and visualizations respectively.
provided in Section Dn2 Figure 8
D.I General Task Attributes
Generalization: To present the characteristics changeable visual signals in real scenes, we build dozens of novel environments for each task, comprising various targets and task- related or environmental distractors. Also, dozens base environments are built as training resources, enhancing AED methods" generalization ability. Letting them train in base environments with different domains can also be regarded as domain randomization technique [9), preprocessing for future sim2real experiments.
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Table 5: FSI task configurations
# of stages
# of epochs
# base env.
# of novel env.
# of task distractors
# of env. distractors
Task
Close Drawer
160
18
18
Press Button
40
27
18
0
Pick Place
50
90
42
Move Glass Cup
42
20
Organize Table
3
50
49
25
Back to Box
3
50
60
18
0
Factory Packing
80
36
12
0
Close Drawer
Pick Place
Organize Table
environment environment
base
novel
start
end
start
end
start
end
Press Button
Move Glass Cup
Back to Box
base environment environment
novel
start
end
start
end
start
end
Figure Visualizations of our designed indoor FSI tasks. The backgrounds (wall, floor) and interactive objects are disjoint between base and novel environments.
Figure 9: Progress visualization of our Factory Packing task. The agent needs to wait for the box's arrival. then places the product into the box and lets the box move the next conveyor belt.
Challenging: Three challenges, including multi-stage tasks, misaligned demonstrations, and different appearances between expert and agent, are included in our FSI tasks. Moreover, unlike existing benchmarks (e.g., RLBench (41)) attaching the objects the robot while grasping, we turn on the gravity and friction during the whole process of simulation, so grasped objects may drop due unsmooth movement.
Our tasks support multi- lighting, soft shadow, and complex object texture to make them closer to reality (cf. Figure 99. Existing benchmarks usually have no shadow (or only hard shadow) and simple texture. Besides, all objects are given reasonable weights The aforementioned gravity and friction also make our missions more realistic.
Realistic:
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D.2 Task Deseriptions
Close Drawer ( [Isttage, indoor)
Description
Fully close the drawer that was closed by the expert in demonstrations. Besides, the objects the table will be randomly set in the designated area. The target drawer is fully closed, and another drawer (distractor) must not be moved.
Success condition
Behavior errors
(1) Not fully close the target drawer; (2) Close the distractor, not the target drawer; (3) Close two drawers at the same time.
Press Button (1-stage, indoor)
Description
Fully press the button which is randomly placed in a designated area.
Success condition
The button is fully pressed.
Behavior errors
(1) Not touching the button at all; (2) The button is not fully pressed.
Pick & Place (2-stages, indoor)
Description
Pick and place the mug/cup into the bowl the expert placed in demonstra- tions.
Success condition
The mug/cup fully placed the target bowl.
Behavior errors
(1) Failed to pick up the mug/cup: (2) Failed to place into the target bowl;
(3) Misplaced the mug/cup into another bowl (distractor).
Move Glass Cup (2-stages, indoor)
Description
Pick up and place glass of water on the coaster the expert placed in demonstrations.
Success condition
The glass of water is placed on the target coaster and no water is spilled. (1) Failed to pick up the glass of water; (2) drips spilling; (3) Not placed on the target coaster.
Behavior errors
Organize Table (3-stages, indoor)
Description
Pick up and place the object in front of the target bookshelf, and push under the shelf.
Success condition
The object is fully under the target shelf. (1) Failed to pick up the object; (2) Losing object during moving; (3) Not fully placed under the target shelf.
Behavior errors
Back to Box (3-stages, indoor)
Description
Pick up the magic cube/dice and place it into the storage box. Then, push the box until it is fully under the bookshelf.
Success condition
The magic cube/dice is in the storage box, and the box is fully under the bookshelf.
Behavior errors
(1) Failed pick up the magic cube/dice. (2) Failed to place the magic cube/dice into the storage box. (3) The box is not fully under the bookshelf.
Factory Packing (4-stages, factory)
Description
Wait until the product box reaches the operating table, place the product in the box, and pick and place the box the next conveyor belt.
Success condition
The product is inside the box, and the box reaches the destination table.
Behavior errors
(1) Failed to place the product into the box. (2) Failed to pick up the box.
(3) Failed to place the box the next conveyor belt.
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E Additional Experimental Results
This section reports more experimental results. We introduce the accuracy function used for deter- mining an error prediction result in Section E.T] Furthermore, more details on the main experiment are provided in Section E2 Next, all ablations are summarized in Section DX
At last, the pilot study
on error correction is shown in Section 6.3
E.1 Accuracy Function
lout successfu?
error
marked as
faise positive IFP)
negative (TN)
true positive (TP)
negative (FN)
Figure 10: Rules in the aceuracy function A(.:) to determine the error prediction results.
As stated in the main paper, our control and understanding are diminished in novel environments. Consequently, during inference, we lack frame-level labels, only discerning success or failure at the end of the rollout. We adopt similar rules from [9] to define the accuracy function A(,,), -). as depicted in Figure TO. This function determines the outcome of error prediction at the sequence-level, which may not adequately reflect the accuracy of timing for error detection. Hence, we proceed to conduct the timing accuracy experiment (Figure in the main paper) to address this concern.
E.2 Main Experiment's Details
Table 6: FSI policy performance. Success rate (SR) [1] and its standard deviation (STD) are reported. STD is calculated across novel environments, rather than multiple experiment rounds. This accounts for the variability in STDs, as the difficulty of novel environments within the same task can vary.
Close Drawer
Press Button
Pick & Place
Move Glass Cup
NaiveDC
91.11 + 20.85%
51.94 + 18.79%
55.00 + 24.98%
42.25 + 34.04%
DCT
80.56 + 26.71%
80.56 + 11.65% 75.56 12.68%
64.05 + 19.77%
88.00 + 14.87%
SCAN
88.33 13.94%
71.19 15.38%
58.25 + 20.33%
Organize Table
Back Box
Factory Packing
NaiveDC DCT
12.20 + 13.93%
08.89 + 09.21% %
45.42 - 30.92%
79.00 09.27%
29.17 09.46%
88.75 + 07.11%
SCAN
66.60 23.18%
58.89 10.87%
63.75 +: 33.11%
FSI policy performance The policy performance is originally reported in Figure 4f of the main paper. Here, we provide a comprehensive overview in Table Each policy conducts 20 executions (rollouts) for each novel environment to obtain the success rate (SR). Subsequently, the average success rate and its standard deviation (STD) are calculated across these SRs. Large STD values may arise due to the diversity in difficulty among novel environments. Factors such as variations in object size or challenging destination locations can lead to divergent performance outcomes for the policies. Additionally, these rollouts are collected for later AED inference.
In general, the NaiveDC policy achieves the best results in simple tasks because the task-embedding from the concatenation of the first and last two frames provides sufficient information. However, its performance rapidly declines as task difficulty increases. Besides, the DCT policy excels tasks where the misalignment between demonstrations is less severe, as it fuses the task-enbeddings in the temporal dimension. Lastly, the SCAN policy outperforms in tasks with high temporal variability because its attention mechanism filters out uninformative demonstration frames effectively.
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Main experiment's ROC curves We have reported detailed numbers from the main experiment in Figure the main paper. Here, Figure (on page 25) presents the ROC curves of all tasks. The numbers reported in Figure 4 represent repreente the area under the curves. To reiterate, our proposed PrObe achieves the best performance on both metrics and can handle the various behaviors exhibited by different policies.
E.3
Ablation Study
AUROC
AUPRC
NavvoCo
0.9
0.9
-00.49%
-05.07%
+03.93%
+06.45%
-00.77%
+00.03%
-03.32%
-01.5%%
+06.58%
-01.1%
OCT
0.5
0.5
+00.61%
0.9
0.9
SCAN
-02.96%
-05.72%
+13.91%
+18.03%
+03.68%
+03.39%
-01.26%
-20.83%
+25.17%
+01.8%%
SVODED TaskEmbED LSTMED
OCTED
PiObe
SVDDED TaED LSTMED
DCTED
PrObe
Figure 11: Effectiveness of rollout augmentation (RA). AUROC[T] and AUPRCI reported. Methods trained without RA (dark) and with RA(bright) and their performance gap are listed. RA is more beneficial for methods with time information (rightmost three columns), Also, the improvement from RA is more evident when the performance of FSI policy is higher (SCAN case).
Rollout augmentation This experiment aims validate whether rollout augmentation (RA) en- hances the collected agent rollout diversity and, consequently, improves the robustness of error detectors. Figure [1 presents the results of all error detection methods (trained with and without RA) monitoring policies solving the Pick & Place task, yielding the following findings: (1) RA has minor or even negative impact on SVDDED, as expected. Since SVDDED exclusively trained on single frames from successful rollouts, the removal of frames by RA decreases overall frame diversity. (2) RA proves beneficial for methods incorporating temporal information (LSTMED, DCTED, and PrObe), particularly when the FSI policy performs better (SCAN case). This because error detectors cannot casually trigger an error, or they risk making a false- solise--osiivee prediction. The results indicate that methods trained with RA generate fewer false-positive predictions, showcasing improved robustness to various policy behaviors.
0.85
AUROC
AUPRC
0L8012 0.8072
0.80
0.7827
0.7842
0.7904
0.7657
0,7714
0.75
0.7223
0.20
petter ecractor
V
d
J
Lpat
V
I.om
Figure 12: Component contributions. AUROC[1] and AUPRC[T] are reported. The pattern extractor and two auxiliary objectives significantly improve the performance.
PrObe's design verification Figure 12 illustrates the performance contribution of each component of PrObe, observed while monitoring the SCAN policy solving the Pick & Place task. The naive model (first column) removes the pattern extractor and utilizes FC layer followed by an IN layer to transform history embeddings. Clearly, the designed components and objectives enhance performance, particularly with the addition of the pattern extractor into the model. We attribute this improvement to the fact that the extracted embedding patterns by our PrObe more informative and discernible.
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AUROC
AUPRC
NalveDe
0.5
0.3
0.3
0.2256
0.0949
0,0207
0.0377
0.0055
0.1348
0.0541
0.0229
0.0423
0.0093
Policy
0.7
0.7
DCT
0.5
0.5
0.3
0.1020
0,0479
0.0410
0.0063
0.0826
0.0391
0.0419
0.0769
0.0106
0.9
0.9
SCAN
0.7
0.7
0.1082
0,0209
0.0471
0.0296
0.0796
Error Detector
Error Detector
Figure 13: Performance results with error bars (standard deviation, STD). AUROC[T], AUPRC[+] are reported. Here, STDs are computed across multiple experiment rounds (random seeds). is obvious that in the representative Pick & Place task, PrObe not only achieves the highest scores when detecting all policies' behavior errors but also has the best stability (the smallest STD).
Performance stability Our work involves training and testing all policies and AED methods to produce results for an FSI task. Additionally, each task comprises numerous base and novel environments. These factors make it time-cosumiig and impractical to include error bars in the main experiment (Figure of the main paper). Based on our experience, generating the results the main experiment once would require over 150 hours.
Therefore, selected the most representative Pick & Place task among FSI tasks and conducted multiple training and inference iterations for all AED methods. We present the AUROC, AUPRC, and their standard deviations (STD) averaged over multiple experimental rounds (with five random seeds) in Figure [3 From the results, our proposed PrObe not only achieves the best performance in detecting behavior errors across all policies but also exhibits the most stable performance among all AED methods, with the smallest STD values.
label
SVDDED
TaskEmbED
LSTMED
DCTED
PrObe (Ours)
0.5
0.4
0.75
S VODED outpura
Probability
0.3
0.50
0.25
0.1
0.00
0.0
10
20
30
40
timestep
enror
Figure 14: PrObe's 's failure prediction. the Pick & Place task, the rollout terminated immediately if the mug is no longer the table, giving the emror detectors only six frames to detect the error in this case. However, it took a while for our Probe's pattern flow to induce enough change. By the last frame, PrObe was just beginning to increase its predicted probability, but it was too late.
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PrObe's failure prediction We analyze cases in which PrObe is ineffective at detecting behavior errors and visualize the results Figure [4] As stated in the main paper, our PrObe requires short period after the error occurs to allow the pattern flow to induce enough changes. However, in the case depicted Figure[4 the mug had fallen off the table, resulting in the immediate termination of the rollout. Such short duration is insufficient for PrObe to adequately refecc, thereby rendering it unable to substantially increase the prediction probability of behavioral errors at the last moment. Developing mechanisms for quick responsive pattern flow is one of our future research directions.
Table 7: Demonstration quality experiment. AUPRC [t] is reported. This experiment verifies the influence of demonstration quality on both the FSI policy and AED methods. PrObe the only method that achieve higher performance when receiving sub-optimal demonstrations, as the FSI policies raise more failures.
NaiveDC policy
Setting
Success Rate
SVDDED
TaskEmbED
LSTMED
DCTED
PrObe
all optimal
51.94
18.79%
0.6956
0.7280
0.7051
0.8405
0.8851
optimal 2
50.83
18.50%
0.6861
0.7407
0.7168
0.8832
0.9059
all sub-optimal
51.67 - 17.24%
0.6414
0.7276
0.7044
0.8446
0.8930
DCT policy
Setting
Success Rate
SVDDED
TaskEmbED
LSTMED
DCTED
PrObe
all optimal
80.56 11.65%
0.3789
0.3597
0.4468
0.5734
0.7474
3 optimal 2 sub all sub-optimal
78.61 = 14.02%
0,3908
0.3811
0,4606
0.6019
0.7530
77.22 = 14.16%
0.3899
0.4051
0.4718
0.5341
0.7588
SCAN policy
Setting
Success Rate
SVDDED
TaskEmbED
LSTMED
DCTED
PrObe
all optimal
75.56 12.68%
0.4653
0.4657
0.4487
0.6757
0.7505
3 optimal & 2 sub all sub-optimal
71.67 = 11.18%
0.4985
0.5165
0.4979
0.7467
0.7804
69.72 = 12.30%
0.4886
0.5262
0.5096
0.7140
0.7752
Demonstration quality We verify the influence of demonstration quality on both the FSI policy and AED methods, specifically testing it on the Press Button task. We collected sub-optimal demonstrations that failed to press the button on the first trial but successfully pressed it the second try. Then, the policies and AED methods used these demonstrations to perform the task.
We expected the policy performance to decrease as the demonstration quality decreases. Therefore, AED methods should achieve a higher AUPRC score if they are not affected by the quality changes of demonstrations, as it becomes easier to detect errors. However, from Table 7 observe that not all AED methods obtain higher scores, indicating that they are also affected by the decrease in quality.
Notably, our proposed PrObe is the only method that achieves better results for all cases when comparing the first and second rows, and the first and third rows for each policy. Additionally, an interesting observation can be found in the case of NaiveDC policy (comparing all optimal vs. all sub-optimal): L.SMEED and TaskEmbED obtained similar AUPRC scores, as they do not access the demonstration information directly; SVDDED suffered in this situation since it averages every demonstration frames (including those sub-optimal movements); DCTED and our PrObe achieved slightly better results because they filter useful information from the demonstrations.
Table 8: Viewpoint changes. AUROC [1] is reported. We study the impact of viewpoint changes on AED methods' performance. The camera is slightly shifted away from the table during inference, which makes detecting errors more difficult. Nevertheless, our PrObe is the least affected method.
Viewpoint SVDDED
TaskEmbED
LSTMED
DCTED
PrObe
original shift
0.5694
0,5465
0.4929
0.7501
0.7498
0.4821
0.4959
0.4667
0.7056
0.7225
difference
-15.34%
-9,27%
-5.32%
-5.33%
-3.64%
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Viewpoint changes As AED methods detects behavior errors through visual observations, changes in camera viewpoint will affect the ease of observing errors. We conducted the experiment in the Press Button task with the DCT policy and all AED methods. In this experiment, the camera is slightly shifted away from the table and robot arm, which makes detecting errors more difficult. Besides, the policy is also impacted because the policy and AED methods utilize the same camera. Thus, the performance of DCT policy decreases from 80.00 = 10.80% to 78.33 + 11.18%.
From T although the policy yields more behavior errors after camera shifted, the performance of all AED methods decreases due the increased difficulty of detecting errors. Nevertheless, our PrObe is the least affected method, which demonstrates its superiority against other approaches.
F
Limitations
Offiine evaluation in AED inference? Our ultimate goal is to terminate (or pause) the policy instantly when detecting behavior error online. However, in our main experiment, only the trained policies performed in novel environments online, and their rollouts were collected. AED methods later use the rollouts to predict errors for a fair comparison. Nevertheless, we illustrate that PrObe effectively address the online detection challenge through the time accuracy experiment. Additionally, the pilot study error correction involves running AED methods online to promptly correct errors.
No comparison with state-of-the-art methods? While there are no available state-of- -atee-of-thearrr (SOTA) methods for the novel AED task we formulate, we have endeavored to design several strong baselines with various characteristics (cf. Section[CJof the Appendix). These baselines draw inspiration from relevant areas such one-class classification, metric-learning. temporal modeling. Moreover, their number of learnable parameters is ten times that of our PrObe. Nonetheless, PrObe demonstrates superior performance compared to them in extensive experiments.
Additionally, we observed a parallel line of research focused on detecting or correcting robot errors, with some SOTA approaches [5] 49 50 (66. However, approach and theirs have different natures in terms the task scenario. Their methods typically rely on human-in-the-loop guidance, whereas in our setting, the robot operates in unseen environment, and the expert may only be able to provide demonstrations without the capability to correct the robot's failures. Consequently, they not suitable as baselines in this work.
Not evaluated on benchmarks or real-world environments? Due to the practical challenges inherent in FSI tasks and the AED task, have had develop own benchmarks and tasks. Moreover, the lack of real-world experiments is attributed to the following reasons: (1) Deploying the necessary resources for the AED task in the real world is high-cost and requires a long-ten plan due to safety concerns. (2) The FSI policies still struggle to perform complex tasks in the real world, (3) Although PrObe achieves the best performance, there is still significant room for improvement in addressing the AED task. We emphasize that hastily conducting AED and FSI research in real environments may cause irreparable damage.
making assessments less than comprehensive.
Even previous error correction work [19 or related safe exploration research [57 58 59 was conducted in simulation environments, demonstrating that it is reasonable and valuable for to conduct detailed verification in simulations first. As mentioned earlier, we have developed seven challenging and realistic multi-stage FSI tasks, each containing dozens of base and novel environments. To our knowledge, the simulation environments we established are the closest to real-world scenarios in the literature.
Broader Impact
Our work focuses on addressing a critical learning task, specifically the error detection of policy behaviors, with the goal of accelerating the development of safety-aware robotic applications in real- world scenarios. While we have not identified any immediate negative impacts or ethical concerns, it essential for us to remain vigilant and continuously assess potential societal implications as our research progresses.
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Factory Packing
NutveDD policy
Pallr NOS
DC1 Panie
SCAN pollic
SCON Pauier
NOS paliey
Move Glass Cup
Button
Back to Box
DCT policy
DCT palicy
DC Bollis
NaiveDC policy
NaiveDC policy
NaiveDC
SCAN mollyy
Close Drawer
Pick & Place
Organize Table
DCT po loy
DCT
NaiveDC policy
NetiveD palicy
NalveDD policy
Figure 15: AED methods' ROC curves for tasks. The AUROC scores reported in the main paper's Figure Mare are the area under the curves.
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NeurIPS Paper Checklist
1. Claims
Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Question:
Answer: [Yes]
Justification: Our abstract and introduction clearly describe that (1) our AED task is essential and practical, (2) comprehensive benchmark is proposed evaluation platform, and (3) our PrObe is an effective method for the AED task. The contributions and scope of our work are also presented in the introduction.
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It is fine to include aspirational goals as motivation as long as is clear that these goals attained by the paper.
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Question:
Does the paper discuss the limitations the work performed by the authors?
Answer:[Yes)
Justification:
We responsibly present work's limitations in Section F of the Appendix.
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The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
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While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory Assumptions and Proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
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Answer: [NA]
Justification: Our work does not include theoretical results.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental Result Reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results the paper to the extent that affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Question:
Answer: [Yes]
Justification: In addition to the information provided in the Experiment section of the main paper, we describe how reproduce the results of FSI policies, our PrObe, and error detection baselines in Sections A, B, and C of the Appendix, respectively. These details include model architectures, hyperparameters, optimizer settings, and experimental device.
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5. Open access to data and code
Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
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Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
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Reviews:
[{"review_id": "DyKPU9NHXg", "reviewer": "Reviewer_qV6D", "summary": "This paper proposed a task Adaptable Error Detection (AED) that attempts to perform online behavior error detection for FSI policies, it advocate three main challenges comparing to FSAD: novel environment, no notable change for behavior error that AED tries to detect, and it has to be conduct simultaneously with policy execution to ensure timely error detection and damage control. The AED benchmark is provided, and a AED method, PrObe is proposed to solve AED through error prediction based on fusion of task embedding and flow pattern generated from history of rollout. The PrObe achieve good performance compare to baselines, and paper provided comprehensive studies analyzing PrObe error detection timing, it's embedding pattern, the importance of components, the stability of error detection, and provided pilot error correction to demonstrate practicality of the AED task.", "questions": "See above.", "limitations": "The limitation in terms of proposed PrObe is discussed through analysis experiments, which make sense to me.", "rating": 7, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. AED is a important task for FSI research.\n2. The proposed PrObe is very effective in AED task for various tasks.\n3. abundant analysis is provided on PrObe timing, embedding analysis, performance stability, ablation and more.", "weaknesses": "The writing has improvement room:\n1. line 161 - line 162: I am confused: if frame level label is not available, how come the measurement is averaging over actions?\n2. The function of demonstration data is not clearly described in section 5.2, more explanation is helpful.\n\nThe AED benchmark is not well described in the writing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer qV6D,\n\nThank you for your insightful comments and high recognition of our work! Our responses below are dedicated to addressing your questions and concerns.\n\n---\n\n**[W1] If frame-level label is not available, how come the measurement is averaging over actions?**\n\n- We try to make Eq. 1 a general form, so we express it in such a way that we can obtain detection accuracy across *frames*. However, as we illustrated in the practical scenario (lines 163-166), frame-level labels are often unavailable during the testing phase in practice. Therefore, the A($\\cdot, \\cdot$) we use in testing is sequence-level. In addition, we conduct the timing accuracy experiments to verify the accuracy of different methods in determining the timing of error occurrences.\n- We will revise this part to make it more clear. Thanks for the question.\n    \n**[W2] The function of demonstration data is not clearly described in section 5.2.**\n\n- The demonstration data originally served as important information for the few-shot imitation policy to understand how to perform tasks in novel environments. In our newly proposed AED task, these demonstrations are also used for AED methods to determine whether the current state deviates from the demonstrations.\n- We agree with the reviewer that we can provide more explanation on the function of demonstration data in our AED task and will include it in our final version. Thank you for pointing out this issue.\n    \n**[W3] The AED benchmark is not well described in the writing.**\n- We have provided the details of our AED benchmark in **Section D of the Appendix**, including the task attributes, each task's description, success conditions, and potential behavior errors. We also refer readers to Section D of the Appendix when introducing evaluation tasks (Section 6.1, line 246) in the main text. \n- If there are any additional details about our AED benchmark that the reviewer is interested in, please feel free to let us know.\n\n---\n\n- Thank you again for your time and dedication in reviewing our paper. We appreciate your positive assessment of our work.\n- If you have any unresolved concerns after reading our responses, please let us know. We look forward to learning more from you during our constructive discussions.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "yysKKtwB8k", "reviewer": "Reviewer_3VgP", "summary": "This paper introduces Adaptable Error Detection (**AED**) within Few-Shot Imitation (**FSI**) tasks, a critical yet underexplored area in robotics and AI. The authors establish a novel benchmark for assessing AED methods and present **PrObe**, designed specifically for this task. Through comprehensive evaluations, the paper demonstrates that PrObe outperforms baseline models and shows robustness across different FSI policies.", "questions": "Q1. Why did the authors choose the main conference track instead of the Datasets & Benchmarks track if they propose a benchmark?  \nQ2. What is the intervention method employed at critical moments to the agent’s policy to collect failed rollouts?  \nQ3. Why were TaskEmbED and LSTMED chosen among the 4 baselines for embedding visualization?  \nQ4. Is AED only applicable for error detection on demonstration-conditioned policies or does it extend to more general policies?  \nQ5. Is PrObe also able to evaluate policies that have been trained for robotic manipulation on proprioceptive data, not image data?", "limitations": "The authors have extensively discussed the limitations of their work.", "rating": 7, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "**S1. Well-written.** The paper has a coherent narrative and logical flow. The problem scenario is effectively motivated with real-life examples that illustrate the practical importance and applicability of the research. Relevant literature is extensively discussed and consistently referenced throughout the paper.\n\n**S2. Illustrations.** The schematic illustrations provided in the paper are well made and enhance the comprehensibility of AED, PrObe, and their experimental analysis. The figures effectively delineate the structured protocol of the AED task across different phases, and the intricate architecture of PrObe, showcasing its components and their functions.\n\n**S3. Unique challenges.** The unique challenges established by AED distinguish it from FSAD and other tasks in prior works. AED's focus on online anomaly detection in novel environments, where behavioral errors occur without clear visual cues, establishes it as a critical area of research.\n\n**S4. Novel components.** PrObe establishes the usefulness of its design principles by outperforming other baselines. Augmenting rollouts, extracting patterns from policy embeddings, generating a pattern flow and fusing it with task-embeddings, and utilizing a temporal-aware triplet loss - all contribute to its effectiveness.\n\n**S5. Comprehensive analysis.** The paper extensively explores PrObe's application in FSI policies for AED. PrObe achieves better visual separability of features in the latent space between successful and erroneous trajectories, thereby enhancing the interpretability of learned embeddings. Notably, PrObe excels in temporally precise error detection, outperforming other methods that detect errors too late or too early. An in-depth ablation study confirms the stability and essential contributions of PrObe’s components. Additionally, the paper explores PrObe’s failure scenarios, the impact of demonstration quality, and viewpoint changes.", "weaknesses": "W1. While the textual descriptions in the Preliminaries section provide a solid understanding of the DC policy, incorporating mathematical notation would significantly enhance clarity and comprehension. Specifically, the paper could benefit from the following mathematical formulations:  \n 1. The contents of history $h$, detailing what it encapsulates\n 2. The feature encoder and task-embedding network computing the history, demonstration, and task-embedding features\n 3. Task-embedding network padding the sequences\n 4. The actor policy, e.g, $\\pi(a|o_t, f_h, f_{\\zeta})$\n 5. The inverse dynamics module\n 6. NLL and MSE objective in this particular setting\n\nW2. The average difference metric presented in Figure 4, which compares each method's performance to the second-best performing method, may be inadequate. This approach is only sensible when comparing two methods. A more informative assessment might measure performance gains relative to the worst-performing method, or performance losses relative to the best-performing method. Alternatively, using a standardized baseline or oracle for comparisons could provide a clearer and more meaningful evaluation.\n\nW3. PrObe is only shown to be effective for evaluating policies trained on image data. As a primary example, the authors motivate the FSI ED problem setting on robotic tasks. Although sensor data cannot always fully capture the properties and locations of separate objects, policies for robotic manipulation tasks are often trained on proprioceptive data—such as joint angles and torques—to achieve better precision. Image data, though rich in environmental context, can sometimes be unavailable or impractical due to factors like occlusions, lighting conditions, and higher training costs. The inherent differences between image-based and proprioceptive data mean that the latent space characteristics and the patterns critical for error detection would vary. Image-based models capture visual features like shapes and spatial relationships, whereas proprioceptive-focused models emphasize dynamics and kinematic features, including the robot’s mechanical interactions. While there's no inherent reason to doubt PrObe’s capability with different data modalities, the effectiveness of its pattern extractor when applied to latent features learned from proprioceptive data remains uncertain without empirical validation. Conducting experiments with proprioceptive sensor data and demonstrating successful results would further validate the robustness, generality, and applicability of PrObe.\n\n### Minor Improvements\n\n1. The second and third contributions—introducing a novel benchmark and PrObe—are blended together; these could be separate for clarity.\n2. Please add a reference in the main paper that the limitations are in Appendix F.\n3. Line 125 “semantically” refers to language or linguistics, which doesn’t seem to be the intended use of the word here\n4. Line 133 “learns implicit mapping” → “learns an implicit mapping”\n5. Line 136 “current history” → “the current history”\n6. Line 154 “and few” → “and a few”\n7. Line 168 “high controllable” → “highly controllable”\n8. Line 174 “and few” → “and a few”\n9. Line 192 “from agent’s” → “from the agent’s”\n10. Line 205 “take as input the history features $f^h$” →“takes the history features $f^h$ from … as input”\n11. Footnote on page 4 “thet” →”they” \n12. Line 614 “are are” → “are”\n13. Line 661 “*Pick & Plcae*” → “*Pick & Place*”", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer 3VqP,\n\nThank you for your insightful comments and high recognition of our work! Our responses below are dedicated to addressing your questions and concerns.\n\n---\n\n**[W1] Incorporating mathematical notation would enhance clarity and comprehension.**\n\n- Thank you for your suggestion. We will add mathematical notations to the cases you listed. For example, we will use $h_t \\coloneqq (o_0, o_1, \\ldots, o_t)$ for the rollout history at time $t$, where $o$ represents the observation. We will also review the paper to identify any parts that can be enhanced for clarity and comprehension.\n\n**[W2] The average difference metric presented in Figure 4 may be inadequate.**\n\n- Thank you for pointing out this important issue. As per your suggestion, we provide the average performance difference metric comparing each method with the **worst** and **best** methods in the following tables.\n\n    Comp. w/ the worst method:\n\n    |  | SVDDED | TaskEmbED | LSTMED | DCTED | PrObe |\n    | --- | --- | --- | --- | --- | --- |\n    | AUROC | 7.05% | 41.63% | 41.59% | 44.20% | 67.29% |\n    | AUPRC | 2.61% | 35.42% | 61.67% | 57.13% | 78.03% |\n\n     Comp. w/ the best method:\n\n    |  | SVDDED | TaskEmbED | LSTMED | DCTED | PrObe |\n    | --- | --- | --- | --- | --- | --- |\n    | AUROC | -34.87% | -16.90% | -15.95% | -15.44% | -2.45% |\n    | AUPRC | -32.28% | -19.63% | -10.87% | -13.17% | -1.06% | \n- From the results under these two new metrics, it's obvious that our PrObe method significantly outperforms the compared baselines. We will include these new results in our final version.\n\n**[W3, Q5] Is PrObe also able to evaluate policies trained on proprioceptive data?**\n    \n- Thank you for the interesting question. We agree that it is worth exploring whether PrObe can observe pattern changes in proprioceptive data (data in another modality). However, considering our work's setting (refer to the scenario presented in Section 4), it may not be feasible to conduct experiments on proprioceptive data for two main reasons:\n\n1. The configurations of experts and agents are different. It may be very challenging for few-shot imitation (FSI) policies to learn tasks from different joint sets. As you mentioned, the proprioceptive data may not provide all the necessary task-related information that could potentially be extracted from image data.\n2. Our setting assumes that we have less information and control in novel environments. Even if we assume that the error detector and FSI policy can be integrated into the same agent, we may not be able to access the expert's proprioceptive data (for example, if the expert is a customer).\n\n- Although it is difficult to conduct experiments on training PrObe with proprioceptive data under our current problem setting, we will explore whether PrObe has the ability to learn from data of different modalities in the future. \n\n**[Q1] Why did the authors choose the main conference track instead of the Datasets & Benchmarks track?**\n    \n- Thank you for the question. In this work, we regard the AED task as a new learning problem. In addition, we propose a new method, PrObe, to address this challenging task. The AED benchmark is a platform to verify the effectiveness of various AED methods. For us, the first two parts (a new task and a new method) are the main contributions of this work, and thus, we decide to submit it to the main track.\n\n**[Q2] What is the intervention method employed at critical moments to collect failed rollouts?**\n- Thank you for the question. Different intervention methods are designed for different tasks, which can be roughly divided into two categories: (1) The interactive object is correct, but the interaction fails (e.g., failure to grasp the object, failure to press the button, failure to close the drawer), and (2) Interacting with the wrong objects (e.g., placing objects in the wrong container, closing the wrong drawer).\n- For the first case, we add a random shift to the accurate pose. For the second case, we replace the accurate pose with the pose of the incorrect object, which also includes a random shift. We will include these details in each task's description.\n\n**[Q3] Why were TaskEmbED and LSTMED chosen for embedding visualization?**\n\n- Due to space constraints when drafting the paper, we  report two baselines with the most different attributes in our embedding visualization experiment (cf. Table 3 in the Appendix). However, we can provide visualization results for all baselines. Please refer to the PDF file in our global rebuttal for more details. We will integrate all visualization results into our final version.\n\n**[Q4] Is AED only applicable for error detection on demonstration-conditioned policies?**\n\n- Thank you for the question. In the problem setting we are considering (recall the scenario provided in Section 4), the policy has to learn the task through observing demonstrations performed in a novel environment. Mainstream few-shot imitation policies following this setting are usually demonstration-conditioned, so our AED task and Probe method focus on detecting the errors caused by this type of policy.\n- If the policy can now be directly trained and tested in a single scene, e.g., conventional imitation learning, then intuitively, our Probe can work in this setting by simply removing the fusion process of pattern flow and task embeddings. In other words, Probe would use only the pattern flow to detect errors. We will continue to explore how to improve the versatility of Probe for various policies.\n\n**Typos & minor improvements**\n\n- Thank you so much for your careful review of our paper. We will fix these typos and revise the paper following your instructions.\n---\n- Thank you again for your time and dedication in reviewing our paper. We appreciate your positive assessment of our work. \n- If you have any unresolved concerns after reading our response, please let us know. We look forward to learning more from you during our constructive discussions.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Dx6OkyXO1F", "reviewer": "Reviewer_SywQ", "summary": "The paper aims to address the adaptable error detection problem, i.e., detecting the error behavior of a robot policy in an unseen environment. Solving the problem ensures that the robot is stopped before performing behavior that causes disruptions to the surroundings.\n\nThe AED problem introduces three challenges: the abnormal behavior in the unseen environment is not observed in the training data and common anomaly detection method cannot be used; with complex background, there is no noticeable change between frames and this makes it difficult to indicate when the error occurs; AED is required to terminate the policy timely once the error occurs. The three challenges make it difficult to apply the current approaches on anormaly detection.\n\nThe paper creates a new benchmark specially designed for adaptable error detection, which consists of 322 base and 153 new environments encompassing six indoor tasks and one factory task.\n\nThe paper proposes a PrObe architecture consists of a pattern extractor and a flow generation equipped with a pattern loss, a temporal contrastive loss and a classification loss to predict the error probability.\n\nThe paper conducts experiments on the proposed benchmark and achieves the top 1 counts, average ranking, and average performance difference. Extensive ablative study justifies the effectiveness of the design choices.", "questions": "On line 207-208, normalizing by L2 norm could make all the features have the same norm but how could this mitigate the biases? Even the unit vector may only learn the background information in the visual inputs.\n\nOn line 212-215, the authors need to note that the task embeddings are extracted using prior works, which is introduced only in the experiments.\n\nCould the authors provide more details or some examples on how to collect the positive and negative pairs for L_{tem}?", "limitations": "None", "rating": 7, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Few-shot imitation learning learns a robot policy for a new environment with only a few demonstrations, which could accelerates the development of robot applications. However, with only a few demonstrations, there exist many corner cases that the robot could not observe in the demonstrations and the appearance and background of the scene are also different. This makes the policy perform error bahavior, which makes the surroundings fall into a dangerous situation. Thus, the problem of adaptable error detection is important to solve.\n\nThe three loss designs not only supervisedly learn the error prediction but also unsupervisedly regulate the intermediate feature to follow some human priors including close features for frames with high task relation and sparse feature to detect frame changes.\n\nThe experiment datasets and tasks are specially designed for the AED problems and selected baselines are strong error detection baselines.", "weaknesses": "One main contribution of the paper is that error detection could avoid the robots making disrupted behavior in the real-world environment. Thus, the authors may need a real-world environment that contains some disruption scenarios and test the method in such environments. Even the most realistic simulation may not simulate some real factors such as inaccurate control program of the robot arm.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer SywQ,\n\nThank you for your insightful comments and high recognition of our work! Our responses below are dedicated to addressing your questions and concerns.\n\n---\n\n**[W1] The authors may need a real-world environment that contains some disruption scenarios and test the method in such environments.**\n    \n- Thank you for the suggestion. As we discussed in the Limitation section (lines 745-758), there are still several obstacles to performing our AED task in real-world environments. Considering the capabilities and safety concerns of few-shot imitation (FSI) policies and AED methods in real-world environments, we first verified the AED tasks in realistic simulated environments. This approach was also inspired by previous work [19] and related research fields (e.g., safe exploration [57, 58, 59]).\n- We will continue to advance the progress of our AED task, including future sim-to-real and real-world experiments. Thank you again.\n\n**[Q1] On line 207-208, normalizing by L2 norm could make all the features have the same norm but how could this mitigate the biases?**\n\n- According to our experience, the feature values extracted from observations in different environments vary greatly, and such deviations may exacerbate errors in model judgment. Therefore, we divide the extracted features by their L2 norm to turn them into unit vectors, and let the subsequent pattern observer learn on the unit vectors. This can eliminate the deviation in value scale caused by domain (environment) changes. However, unit vectors from different domains may still have angular deviations, so in lines 207-208 we use the term \"mitigate\" rather than \"eliminate.\"\n\n- We will continue to explore advanced strategies in domain adaptation to further mitigate the effects of domain change on the model. Thank you for this question.\n\n**[Q2] On line 212-215, the authors need to note that the task embeddings are extracted using prior works, which is introduced only in the experiments.**\n\n- Thank you for the suggestion. As you mentioned, these task embeddings are extracted from various few-shot imitation (FSI) policies, so how they are extracted can only be introduced when discussing which FSI policies are ultimately used. However, regarding the function of task embeddings (demonstrations) in our AED task (also mentioned by Reviewer qV6D), we can indeed provide more explanation in Section 5.2. \n- Please check our response to Reviewer qV6D's W2 for more details. Thank you again.\n\n**[Q3] Could the authors provide more details or some examples on how to collect the positive and negative pairs for $L_{tem}$?**\n\n- Thank you for the question. First, please note that according to our setting, the failed rollouts $X^b_{fail}$ in the base environment have frame-level labels. For each sampled failed rollout in each training iteration, we first randomly select one frame as the anchor. The anchor may be a normal frame or a frame where the error has occurred. Based on the selected anchor, we will sample the positive and negative samples using the frame-level labels of the failed rollout. After selecting the three samples, we calculate the temporal distance between the anchor and the positive sample, and the anchor and the negative sample. Then, we adjust the temporal-aware margin in $L_{tem}$  based on these temporal distances (line 230). Through this process, our $L_{tem}$ objective can simultaneously consider the latent and temporal correlations between frames.\n\n- We will include these details in our final version. Thank you again for this question.\n\n---    \n\n- Thank you again for your time and dedication in reviewing our paper. We appreciate your positive assessment of our work. \n- If you have any unresolved concerns after reading our responses, please let us know. We look forward to learning more from you during our constructive discussions.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
We would like to thank all reviewers for their constructive and insightful comments on our work.

We are excited that our work possesses several strengths recognized by the reviewers, as summarized below:

- Our proposed AED task is **important** (all reviewers) with **unique challenges** (3VgP).
- Our proposed solution PrObe contains **novel components** (3VgP) and is **effective** (all reviewers).
- Our work provided comprehensive analysis (qV6D, 3VgP); the compared **baselines** are **strong** (SywQ).
- Our paper is **well-written** (3VgP) with **well-made illustrations** (3VgP).

Regarding the concerns and questions raised by the reviewers, we have provided **more details** and **evaluation results under new metrics** to address them. Additionally, we have analyzed the feasibility of conducting the studies suggested by the reviewers. Finally, we will refine our paper based on the improvement suggestions from all reviewers.

We have posted our responses under each reviewer's comment separately. Please let us know if there are still any unresolved concerns.

Thank you again for reviewing our paper.

---

In response to the reviewer's inquiry, the attached PDF provides embedded visualizations of all baselines. Please check the explanation in the PDF with Table 3 in the Appendix.

Meta-review: This paper is about a new task, adaptable error detection, in context of few-shot imitation policy learning, where the task is to detect behavior errors made by the few shot imitation policy. from visual observations in unseen environments.

The paper proposes a new task (AED), an associated benchmark, and a novel model to solve the AED task called PrObe.

All reviewers agree that the paper proposes a novel task (AED) and approach (PrObe), the AED task is very relevant for few-shot imitation learning, it introduces new challenges, and PrObe contains novel components. The paper is well written, and evaluation seems correct. This AC agrees with these evaluations.

The paper needs some minor improvements, mainly to clarify benchmark details, and possibly shuffle some details between supplementary and main paper, to make sure the important details are in the main paper, and the mathematical descriptions of tasks and models. The rebuttal and discussion provide detailed feedback to improve the paper.

Overall this paper is considerably above the acceptance threshold, and should be accepted.
Decision: Accept (poster)