Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation
Authors: Daehee Lee
Minjong Yoo
Woo Kyung Kim
Wonje Choi
Honguk Woo
Venue: NeurIPS 2024 poster (2024)
Keywords: Continual Imitation Learning
Unlearning
Primary area: reinforcement_learning
TL;DR: Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation

Abstract: Continual Imitation Learning (CiL) involves extracting and accumulating task knowledge from demonstrations across multiple stages and tasks to achieve a multi-task policy. With recent advancements in foundation models, there has been a growing interest in adapter-based CiL approaches, where adapters are established parameter-efficiently for tasks newly demonstrated. While these approaches isolate parameters for specific tasks and tend to mitigate catastrophic forgetting, they limit knowledge sharing among different demonstrations. We introduce IsCiL, an adapter-based CiL framework that addresses this limitation of knowledge sharing by incrementally learning shareable skills from different demonstrations, thus enabling sample-efficient task adaptation using the skills particularly in non-stationary CiL environments. In IsCiL, demonstrations are mapped into the state embedding space, where proper skills can be retrieved upon input states through prototype-based memory. These retrievable skills are incrementally learned on their corresponding adapters. Our CiL experiments with complex tasks in the Franka-Kitchen and Meta-World demonstrate the robust performance of IsCiL in both task adaptation and sample-efficiency. We also show a simple extension of IsCiL for task unlearning scenarios.

Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation
Daehee Lee M Minjong Yoo*, Woo Kyung Kim Wonje Choi Honguk Woo
Sungkyunkwan University Carnegie Mellon University fdulgi7245, mjyoo2, kwk2696, wjchoi1995, hwoo)Gskku, edu
Abstract
Continual Imitation Learning (CiL) involves extracting and accumulating task knowledge from demonstrations across multiple stages and tasks to achieve a multi-task policy. With recent advancements in foundation models, there has been growing interest in adapter- based CiL approaches, where udapters are established parameter-effiiienll for tasks newly demonstrated. While these approaches isolate parameters for specific tasks and tend to mitigate catastrophic forgetting, they limit knowledge sharing among different demonstrations. We introduce IsCiL, an adapter-based CiL framework that addresses this limitation of knowledge sharing by incrementally learning shareable skills from different demonstrations, thus enabling sample-efficient task adaptation using the skills particularly in non-stationary CiL environments. In IsCiL, demonstrations are mapped into the state embedding space, where proper skills can be retrieved upon input states through prototype-bast bbsed memory. These retrievable skills are incrementally learned on their corresponding adapters. Our CiL experiments with complex tasks in Franka- Kitchen and Meta- World demonstrate robust performance of IsCiL in both task adaptation and sample- efficiency. We also show simple extension of IsCiL for task unlearning scenarios.
Introduction
Lifelong agents such as home robots are required to continually adapt to new tasks in sequential decision-making situations by leveraging knowledge from past experiences. However, many real- world domains pose substantial challenges for these lifelong agents; the complexity and wer-chhnging nature these tasks make difficult for agents to constantly adapt, leading difficulties in retaining knowledge and maintaining operational efficiency [[]] For instance, a home robot agent, operating within a single houschold, needs continuously adapt, learning specific tasks in various areas such as cooking assistance in the kitchen or cleaning in the bathroom. At the same time, it is crucial that the agent not only retains but also improves its proficiency in the tasks has previously learned, ensuring that it maintains consistent efficiency throughout the home.
For these lifelong agents, Continual Imitation Learning (CiL) has been explored, in which an agent progressively learns series of tasks by leveraging expert demonstrations over time to achieve a multi-task policy. Yet, CiL. often encounters practical challenges: (1) the high costs and inefficiencies associated with comprehensive expert demonstrations (2] that are required for imitation, (2) frequently shifting tasks in dynamic, non-stationary environments, and (3) privacy concerns (3] related to learning from expert demonstrations. In this context, CiL faces significant issues in terms of cost, adaptability. and privacy, complicating its implementation real-workee scenarios.
Corresponding author: Honguk Woo (hwoo@skku.cdu) Dachce Lee is currently a visiting scholar Camegie Mellon University.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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To address these challenges, our work focuses on incorporation of skill learning and fine-tuning in CiL, leveraging recent advancements in foundation models 4 [.. These have been increased interests in continual task adaptation based on multiple adapters learned on a foundation model 7 The adapter- based learning approach allows for parameter isolation for individual tasks, thus enabling to mitigate catastrophic forgetting of previously learned knowledge in CiL. Motivated by this use of adapters, we develop IsCiL, a new adapter-based CiL framework that addresses the practical challenges of CiL aforementioned, by incrementally learning shareable skills from different demonstrations through multiple adapters. IsCiL facilitates sample-efficient task adaptation using the skills particularly in non-stationary CiL environments.
Specifically, in the IsCiL framework, a prototype- based skill incremental learning method is employed with a two-level hierarchy including smaller, more manageable adapters: skill retriever and skill decoder. The skill retriever is responsible for composing skills to complete given goal-reaching tasks. It utilizes skill prototypes, which are representative embeddings of skills, to retrieve the appropriate skill for input. The knowledge of each skill contained within the adapter, which can modify its associated base model output. The skill decoder is responsible for producing short-horizon actions for state-skill pairs.
We evaluate IsCiL and several adapter-based continual learning baselines across scenario variations based on complex, long-horizan tasks in the Franka Kitcha- and sample efficiency, task adaptation, and privacy considerations. The baselines include adapter- based continual adaptation techniques as well as conventional continual imitation learning methods. Our results demonstrate that IsCil. achieves robust performance without requiring comprehensive expert demonstrations. This flexibility allows IsCiL to continually and efficiently adapt to varying sequences in different environments by leveraging any available expert data to learn useful skills, with tasks composed of diverse instructions and demonstrations.
environments assess
In summary, the IsCiL framework enhances sample efficiency and task adaptation, effectively bridging the gap between adapter-based CiL approaches and the knowledge sharing across demonstrations. Comprehensive experiments demonstrate that IsCiL outperforms other adapter- adapter-based continual learning approaches in various CiL scenarios.
2 Related work
Continual imitation learning. To tackle the problem of catastrophic forgetting in continual learning, numerous studies have employed rehearsal techniques [8 10 [I] which involve replaying past experiences to maintain performance on previously learned tasks. Another approach involves utilizing
additional model parameters to progressively extend the model architecture [12 13
14
15
[6].
These methods adapt the model's 's structure over time to accommodate new tasks. However, rehearsal techniques exhibit high variability in forgetting depending on the replay ratio and often demand substantial training incorporate new knowledge [17]. Progressive models, on the other hand, require stage identification during evaluation and often overlook unseen tasks [13. In this work, we propose CiL framework that enables effective learning and expansion without requiring rehearsal and stage identification, leveraging pre-trained goal-based model knowledge.
Continual task adaptation with pre-trained models. Several recent works use pre-trained models, accumulating knowledge continually through additional Parameter Efficient Tuning (PET) modules such adapters 18 17 19
6, (22). These methods enhance the fiexibility and scalability of continual learning systems. However, they suffer from inaccurate matching between adapter selection and trained knowledge, leading to misalignment between the knowledge learned during training and the knowledge used during evaluation [17 20), which hinders overall performance. In the realm of sequential decision making, some studies have explored adapting pre-trained models. In (6), the state space of tasks is fully partitioned, restricting its applicability in more integrated environments. Meanwhile, 7 relies on comprehensive demonstrations for learning, which may be impractical in real-world scenarios. Our study aims to enhance task adaptation efficiency by using incrementally generalized skills with accurate matching on state space.
20 21
Skill adaptation. Reinforcement learning research has enhanced fast adaptation through skill ex- ploration (23 and skill priors [24] focusing on improving sample efficiency with offline datasets. Despite these advancements, adapting fixed skill decoders to new environments remains challenging. To overcome these limitations, skill-based few-shot imitation learning methods have been developed

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prosotype-based skill incremental learning
(ii) Task-wise selective adaptation
CIL Stage Incomplete demo. of
CIL Stage N: Incomplete demo, of In
Adaptation Stagie Unseen lask
missing
missing
Start
05
Dore
Skills
gir) Skills
Skills
Task adeptation
Fey Re Rs OS As.
Siail Retriever
Usatle
Retiever
T2
Missing
Skill Retriever
Leamed skill
Skill Decoder
Similar skill referencing
Skill Decoder
Skill Decoder
Action
Action
evaluation
Retrieved skills
Retrleved skills
Task v2 Fail -
Task
Task Success
Task Success
Success
Skill incremental lsarning
Complementing missing parts through usable skill retrieval
Task adegtation via learned skill retrieval
given demonstrations
The scenario demonstrating how IsCiL enhances continual imitation learning efficiency through retrievable skills: (i) Prototype- based skill incremental learning: despite the failure of Tı. skills are incrementally learned from the available demonstrations. In later stages, missing skills for are retrieved from other tasks, achieving the resolution of тι and illustrating the reversibility and efficiency of retrievable skills. (ii) Task-wise selective adaptation: IsCiL effectively retrieves relevant learned skills, facilitating rapid task adaptation.
Figure 1:
25 [6]. However, these methods require extensive past data and struggle with scalability and gener- alization. Even skill-based approaches used in continual imitation learning { still require rehearsal data to mitigate knowledge loss and face difficulties addressing privacy issues through unlearning. Our IsCil. employs parameter-efficient skill adapters to prevent catastrophic forgetting and maintain efficiency, providing scalable solution for unlearning.
Approaches
Our work addresses three key challenges of CiL:: (1) data inefficiency, (2) non-stationarity, and (3) privacy concerns, by adopting retrievable skills in the CiL context. Specifically, our IsCiL framework not only enhances data-efficient continual task evaluation in a non-stationss non-stationary environment but also supports unlearning as a task adaptation strategy, thereby mitigating privacy concerns.
3.1 Problem formulation
In CiL scenarios, consider data stream of task datasets {D, where D, contains expert demonstration D; [d], d," for its associated task т,. To effectively represent complex long- horizon tasks, each task is comprised of sub-goal list, sampled in finite-horizon markov decision process (S, R. Ho. H), where S is state space, A is a action space, / is a transition probability, R. is reward function, f40 is an initial state distribution, and is an environment horizon.
{of ..... M. J. Each task dataset is
For demonstration d = {s,,, a state E S represents a tuple (or, 9t) consisting of an observation o, and a sub-goal 9t- our work, we represent sub-goals through language and use language-basee based goal embeddings for 9, to achieve canguage-condition of IsCiL is to obtain multi-task policy =², by which the performance on the tasks in the data stream can be comparable to that of respective expert policies. This is formulated as
policies. Then, the objective
π argmin
E,
(I)
LTET.
where म, represents an expert policy for 7 and Tr denotes a set evaluation tasks at stage this context, the evaluation tasks continuously vary across different stages.

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3.2 Overall architecture
To effectively handle complicated CiL scenarios, we present the IsCiL framework which involves (i) profotype-based skill incremental learning and (ii) task-wise selective adaptation.
As illustrated in Figure D in (i) the prototype-base skill incremental learning, we use two-level hierarchy structure with a skill retriever n composing the skills for each sub-goal, and a skill decoder Th producing short-horizon actions based state-skill pairs. For this two-level policy hierarchy, employ a skill prototype-har baasye approach, in which skill prototypes capture the sequential patterns of actions and associated environmental states, as observed from expert demonstrations. These prototypes serve as a reference for skills learned from a multi-stage data stream. Using these skill prototypes, we can effectively translate task-specific instructions demonstrations into a series of appropriate skills.
Through this prototype- based skill retrieval method, the policy flexibly uses skills that are shareable among tasks, potentially learned in the past or future, for policy evaluation. This enables the CiL agent to effectively learn diverse tasks and rapidly adapt to variations, while incrementally accumulating skill knowledge from a multi-stage data stream. Furthermore, to facilitate sample-efficient learning and enhance stability in CiL., we employ parameter-effiienn adapters that continually fine-tuned on a base model. Each skill knowledge is encapsulated within a dedicated adapter and incorporated into the skill decoder TD infer expert actions.
In (ii) the task-wise selective adaptation, we devise efficient task adaptation procedures in the policy hierarchy to adapt to specific tasks using incrementally learned skills. This enables the CiL agent to not only facilitate adaptation to shifts in task distribution (e.g., due to non-stationary environment conditions) but also support task unlearming upon explicit user request (e.g., due to privacy concerns).
Suppose that the smart home environment undergoes an upgrade with the installation of new smart lighting systems throughout the house. In this case, task-wise selective adaptation can be used for rapid adaptation by removing outdated control routines associated with the previous systems.
3.3 Prototype-based skill incremental learning
State encoder and prototype-based skill retriever. To facilitate skill retrieval from demonstrations, encode observation and goal pairs (ot, 9t) into state embeddings 8, using function f: (oc: 9t) - st. We implement f as fixed function to ensure consistent retrieval results for learning efficiency, mitigating the negative effects of input distribution shifts.
To effectively handle the mudti-mo of the state distribution in non-stationary environments, we employ skill retriever π- For this, we use multifaceted skill prototypes X= E X' where X' is the set of learned skill prototypes. These prototypes capture the sequential patterns of expert demonstrations associated with specific goal-reaching tasks.
02 = ( X) = (argmax,,
x. 8t), where S(x=; 86) = max sim(b, st)
(2)
Here, h: Xx + 0₂ denotes one-to-one function that maps each skill prototype X to its dedicated adapter parameters 02, while the similarity function 5 is defined as the maximum similarity between state and bases b X= Each X= consists of multiple bases (e.g., 20 bases), and each basis b is representative vector containing its corresponding centroid, shaped identically to the state 8t-
Adapter conditioned skill decoder. To effectively use the knowledge of the pre-trained base model without forgetting, even a non-stationary changing environment, the skill decoder is conditioned based on parameters. The skill decoder policy xn (ā= (0t, Sc: Ope. 02) operates with the skill adapter parameters 02 and the pre-trained base model 0pe. using the Low-Rank Adaptation [27].
Skill incremental learning. To incrementally learn new retrievable skills, we update the skill prototype and adapter pair (X=., 0..) for a novel skill z². The skill prototype X created by dividing a dataset of single skill into several clusters based on similarity. From each cluster, a representative value is extracted serve as the basis b, representing 2*. We use the KMeans algorithm (28 to determine these bases, ensuring that the number of bases \x= adequately captures the diversity within the dataset the novel skill. This multifaceted set of bases allows the skill prototype to capture an accurate multi-modal distribution of the skill represented in the state space, enabling effective retrieval as described in Eq. 2 In our experiment, = is created for each sub-goal g in the given dataset D, for each stage i.

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(a) Prototype-aat based skill retriever Multifaosted skill protoypes (XC)
(b) Skill incremental learning
Inputs (o,,
(0,.0r)
Initiaiize novel skill protutype
Skill Retriever
State Encoder ((r,
SUX.
Dataset
X
0, May 9c)
D
Skill adapter
Skill Rebiever (*,)
airgmax
State Skill adapter mepping h((e)
Xi
0.9
Initialize sovel skill adapter
Skall Decoder
Initialize B(29)
training
TTp (a, 02)
Skill adupter 02
X2
D2-
Pred action as
Figure 2: Overview of the IsCiL framework: (a) The prototype-based skill retriever sequentially utilizes state encoder f. multifaceted skill prototypes X, and skill adapter mapping function h to identify the skill adapter 8z. (b) Skill incremental learning involves the initialization and updating of the skill prototype X=* and its corresponding adapter 82..
The learning of the skill adapter is divided into two phases: initialization and update. During the initialization phase, 0g. initialized using existing skill adapters. Predictions with the existing skill dataset and skill prototypes X= E X' are used to identify the most frequently selected skill. Average scores are computed for each skill prototype using the dataset involved in training 2", as defined in Eq. 2 The skill with the highest average score, denoted as Σ, is selected. Consequently, 0g- is initialized by 0g. Then, the initialized adapter is updated through the following imitation loss.
C(or, St, dr; 02) = ||a xD(ā, ot,9t;
Ir; Ope. 0z)l1, where 0g = TR(0,, 9t)
(3)
The novel skill 2n is incorporated into the learned prototypes X X U Xx*, and the novel prototype and adapter pair (X=*, 0z-) updates the function h for pair mapping. Figure presentss an overview of this methodology. along with the algorithm for incremental learning is detailed in Appendix B.1
3.4 Tisk-wise selective adaptation
Task evaluation. Given the pre-trained model Ppe and learned skill prototypes X, for given inputs (or, gt) from the environment, IsCiL performs the following evaluation process.
â ~ (ā, Ot. gt; Ppres 02), where
(o:
The evaluation process adapts to novel tasks and sub-goal sequences from the environment by modifying the goal 9t- This adjustment enables the inference of appropriate current actions, in manner of similar to handling learned tasks. For example, a kitchen robot tailored to a specific user's s kitchen setup continuously and instantly adapt changes in recipes without additional training.
Task unlearning. To ensure privacy protection for incrementally learned skills, our architecture allows for task unlearning by removing task-specific skill prototypes and adapters. In IsCil., the separation of skill adapters for each task facilitates casy tagging of task information on each skill. When unlearning request given with task identifier the corresponding skill prototypes and adapters are removed. This approach ensures exceptionally efficient and effective unlearning, aligning with the strong unlearning strategies in continual learning discussed in [
4 Experiments
4.1 Environments and data streams
To investigate the sample efficiency and adaptation performance, construct complex CiL scenarios using diverse long-horizon tasks (2 30,3il B1. We then analyze the sample efficiency across different stages and tasks with three types of scenarios: Complete, Semi-complete, and Incomplete, depending on how the samples are utilized and shared. Each scenario consists of a pre-training stage followed by 20 CiL stages. Figure illustrates these scenarios.
Evolving Kitchen. Evolving Kitchen is data stream based on long-horizon tasks the Franka- Kitchen environment 229 30 Each task requires sequentially achieving four out of seven sub-goals. The scenario consists pre-training stage in the environment with only four objects: kettle, bottom burner, top burner, and light switch, followed by continual adaptation to tasks involving seven objects.
5

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CiL Scenario
Scenario calegorization by sample utilization difficulty
Complete
Incomplete
Bemne
Detto
Detoo of
Pento
Evolving kilchen
Low (comprehensive)
Sample utilization difficulty
(incomplete) High
Semi-complete
Deto.
Deno DE
Repeated with different missing parts
CiL
Deno:
Complete detso
Missiing
Evolving World
CiL scenarios including Complete, Semi- Comi-Complete, and Incomplete, categorized by sample utilization difficulty, based on the completeness of the demonstration for task performance: In Complete, each of the 20 CiL stages incrementally introduces new tasks featuring objects not encountered in the pre-training stage, along with full, comprehensive demonstrations for each task. In Commi-Compleie, the first 10 stages are repeated twice, with tasks presented alongside incomplete demonstrations, where specific sub-goals sequence of tasks from the Complete scenario is used, but all stages feature incomplete demonstrations, requiring the system to handle tasks with missing sub-goat trajectories.
Figure 3:
missing from the trajectories. In Incomplete, the same
Evolving World. Evolving World is data stream based the Meta-Worke environment 31 with long-horizon tasks, similar to 332 33 34 Each task requires sequentially achieving four out of eight sub-goals. The scenario consists of a pre-training stage in the environment with only four objects, followed by continual adaptation to an entire environment with all eight Meta-Wel World objects. More detailed configurations are provided in Appendix A
4.2 Baselines and metries
Baselines. We implement continual imitation learning and continual adaptation methods for sequen- tial decision-making problems, which do not use rehearsal. First, consider continual learning algorithms which involve full-model updates (Seq, EWC (35)). We also implement several continual adaptation approaches that utilize pre-trained models with adapters (L2M 66. TAIL (7]). L2M learns key and adapter pair to modulate the pre-trained model, where the key is retrievable state embedding similar to our prototypes. TAIL, unlike L2M, incrementally constructs task identifiers and corresponding adapters to modulate the pre-trained model with new task data without forgetting previous tasks. Each method is categorized based on the values used for adapter retrieval: version that uses no additional identifiers, sub-goat uub-goal identifiers (denoted as -g), and whole sub-goal sequences as single identifiers (denoted as -7). Additionally, we include a Multi-task learning approach as an oracle baseline, which retains all incoming data at each stage and utilizes it for training in subse- quent stages. For all baselines, we use the same pre-trained goal-conditioned policy and a diffusion model (36] 37] as the base policy architecture. A detailed description of the baselines and their hyperparameters are provided in Appendix [3.2]
Metrics. We use three metrics to report CiL performance: Forward Transfer (FWT), Backward Transfer (BW and Area Under Curve (AUC) (3 our long-horizon tasks, these metries rely on conliconditi success rates (GC), which measure the ratio of successfully completed sub-goals to the total sub-goals within each task 3D
FWT (Forward Transfer): This evaluates the ability to learn tasks using previously learned knowl- edge. It is measured by the performance of a task when it occurs.
BWT (Backward Transfer): This evaluates the impact of each learning stage on the performance of tasks learned in previous stages. It measures the change in task performance from past stages observed in the current stage.
AUC (Area Under Curve): This represents the overall continual imitation learning performance a scenario. measures the average performance of tasks learned in the current stage over remaining stages of the scenario.
For all metrics, higher values indicate better performance, with details provided in Appendix B.3

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Table 1: Overall performance on CiL scenarios of Evolving Kitchen and Evolving World: The rows represent baselines, categorized into sequential adaptation and adapter-based approaches, and oracle, respectively. The columns represent continual learning scenarios, where each scenario has 20 stages. Each scenario in the environment is categorized into Complete, Semi-complete, and Incomplete. The highest performance is highlighted in bold and the second highest performance is underlined.
Stream
Evolving Kitchen- Comple--C
Evolving Sitchen-Semi
Evolving Kitchen-
Cil.-algorithm
FWT (8)
BWT (8)
AUC (8)
FWT (8)
BWT (8)
AUC (9)
FWT (8)
BWT (9)
AUC, (8)
Pro-trained
24.3 40.5
29.1 29.1=09
24.3 +0.5
Seq-FT
90,9+26
-63.7=27
35.0=07
37.1-21
-27.1
16.5
32.7+43
-19.6#30
15.7=05
FWC
34.2:08
144
17.1
27.2=13
118.0
12.2
19.3
-3.2
10.4
Seq-LoRA
77.5+26
-55,2+1a
28.3+15
37.4=38
-25,5
15.9=16
32.9+255
-19,9+29
14.5 =0
L2M
24.7.148
-2.5
22.7
19.2
0.2
19.1
17.5144
-2.0.32
15.8
L2M-9
38.2+33
-6,5
32.3
37.9
44.5
32.1
37.5
-6.5
31.0
TAIL-y
85,3180
-49.9.67
41.5
55.0
-21.1:2.2
37.2
53.21177
-20.0120
35.4
TAIL-T
86,2+56
0.0=00
86.2+56
41,2=25
0.0+00
41.2+22
33.8+30
0.0=oo
33.8+3.0
TsCiL (ours)
79.3
11.0216
89.8
68. 1222
8.6
75.8
61.8+0.9
13.7
74.0
Multi-task
93.3+17
-1,6=23
92.3=11
75.4=45
8.0#55
83.2
71.7+1
12.6
83.0=1.
Stream
Evolving World-
Evolving World-Semi
Evolving World- hoold-encomplete
CiL-algorithm
FWT (3)
BWT (8)
AUC (8)
FWT
BWT (s)
(
FWT (5)
BWT
AUC
Pre-trained
0.0=00
0.0=00
0.0=0.
Seq-FT
88.9131
-73.6142
24.9.04
38,9.59
-27.5
13.2.09
41.4
-33.0a20
12.2.03
EWC
25.7
-18.0202
10.5
13.9-14
-9.1
6.2
18.2
-11.62211
8.5
Seq-LoRA
85.6129
175
21.4112
32.2
-18.2144
16.0.23
38.1
-30.6109
11.7.09
1.2M
72.1
-6.622.1
65.9
41.0=a1
6.3130
47.0
I6.1
5.7=28
31.4
L2M-9
64.2#39
-14.3
48.6=20
44,5
3,4+2.
48.2=002
33,2+29
-0.6=09
33.1
TAIL-g
90.0+30
-04.8=04
39.5+22
43.2=78
-17.6=3.5
27.4=5.1
51.4+25
-21.4
32.5+23
TAIL-T
85.7159
0.0..0
85.7.59
27.5
0,0.0.0
27.5
39,711.
0.0
39.7
IsCiL (ours)
81.7+04
2.7=09
84.3
60.0=11
9.3
68.9
63.2
8.7=27
71,2
Multi-task
88.6+36
2.8-35
90.7=11
55.0=36
27.6=4.1
80.9
73.2
12.6
84,2+11
4.3
Overall performance: : sample efficiency
Table [ssows the CiL performance on Evolving Kitchen and Evolving World across three different scenarios (Complete, Semi, Incomplete). We compare the performance achieved by our framework IsCiL and other baselines (1.2M, TAIL)) with different conditioning values (g.7) for adapter retrieval. IsCiL consistently demonstrates superior performance in AUC across all scenarios, achieving between 84.5% and 97.2% of the oracle bascline (Multi-task learning). TAIL-T shows the most competitive performance in the Complete CiL scenario across both environments. However, due to its isolated adapter for learning and evaluation, it fails to effectively utilize samples across stages.
L2M and L2M-g exhibit relatively lower and less stable AUC in the Evolving Kitchen scenario. Conversely, in Evolving World-Semi, they surpass TAIL-T in AUC. This demonstrates that they are capable of sharing different skills across stages. Despite this, they still struggle with accurately retrieving the correct skill or suffer from performance degradation due to knowledge overwriting. Unlike them, IsCiL effectively mitigates overwriting by maintaining distinct skill representations across stages. Both L2M-g and TAIL-g. which aim to leverage sub-goal labels for CiL, struggle to maintain performance due to skill distribution shifts, leading to catastrophic forgetting of skills for sub-goals. These challenges reveal that relying solely on sub-goal labels may not be sufficient to sustain and share skills effectively across different stages and tasks.
Both Seq-FT and Seq-LoRA struggle with forgetting. This is evident in the Complete scenario, where Fe achieves the highest FWT but shows the lowest BWT, leading to decline in overall performance. EWC exhibits consistently lower performance, as the regularization used to preserve past knowledge significantly hinders learning on current tasks, leading to severe degradation in long-horizon tasks. Although FWC shows higher BWT compared to other sequential tuning baselines, its low FWT limits overall effectiveness.
7

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Table 2: Task adaptation performance with unseen tasks: This is based on the existing Evolving World- Complete and Evolving every four stages, while in Evolving Kitchen, two novel tasks are introduced every five stages. Metrics with the suffix -A denote performance based solely on adaptation tasks, while other metrics report performance across all tasks.
Complete. Evolving World, four novel tasks are introduced
Stream
Evalving Kitchen Complete Unseen
Evolving World Complete Unseen
Algorichm
FWT
BWT
AUC
FWT-A
AUC-A (8)
FWT (8)
BWT
AUC
FWT-A (3)
AUC-A (8)
Seq-FT
12
-47.7a16
30.4:02
27.8
19.5
$2,936
-26.7
30.1
16.3
24.0:26
EWC
21.0
-14.0=30
16.8-16
18.1
14,4
16.5+19
-8.1-
9.6826
6.1413
8,3-21
Seq-LoRA
62.4-1
25.4.00
28.1
18.2:00
45.2.n4
-3588
14.5a00
6.4a15
8.2-18
1.2M
22.3
0.3
22.7
15.3
21.2::1
55.1
-1,4
53.6110
40.3
41.2330
1.2M g
33.8 ae
1.3
30.0 .n
22.2 106
24.1
11.
.8.2
35.7
24.2
25.7 11.7
TAIL-9
67.6-14
-34.9554
36.8-32
34.7+22
30.1=11
53.2
-27.1a12
29.2
18.6
19.1
&CCL (ours)
69.5 15
16.3 124
84.4 1.3
$2.1
72.8 1.1
62.
0.5
63.9 1M
45.8 4.
45.3
Multi-task
85.3
3.7=18
88.8.00
70.8
79.0
85,4.n9
5,6
90.4
78.3
85.9
Table 3: Overall performance with task unlearning as task adaptation: Additional stages for unlearning tasks that were learned during other stages are included for tests.
Stream
Evolving Kitchen- Kitchen-Complete Unlcarning
Evolving Kitchen-Incomplete Unlearning
Algorithm
FWT (9)
BWT (s)
AUC (9)
FWT e9)
BWT (8)
AUC
TAII.- CLPU
86,2256 75.0
0.0gan
86.2 85.2
33.8=30 61.4-29
0.0sn0 12.4
33.8
IsCiL (ours)
11.2155
72.7a29
4.4 Task adaptation
Table shows the unseen task adaptation ability of IsCiL., where only the sub-goal sequence of novel task is provided without demonstrations, This scenario extends the existing Complete CiL scenarios by periodically introducing novel tasks. Metrics labeled with the suffix -A indicate results from adaptation tasks, whereas the other metries reflect performance on all tasks. For this scenario, we exclude TAIL-T from comparison, as it lacks the ability to adapt to novel tasks.
IsCiL demonstrates superior task performance in both scenarios, which contributes to greater effi- ciency in task adaptation. Moreover, in Evolving Kitchen, IsCiL not only demonstrates task adaptation ability by achieving the highest FWT-A, but also significantly enhances its initial performance, raising AUC-A of 72.8. TAIL-g shows comparable performance in FWT for the Evolving Kitchen. However, it struggles with catastrophic forgetting, leading to a -34.9 negative BWT when faced with sigmificant distribution shifts in sub-goal demonstrations. In Evolving World, L2M, which actively learns to share skills during training, outperforms TAIL-g. L2M is the only baseline achieving performance improvement on unseen tasks through CiL.
FWT-A from 52.1
4.5 Task unlearning as adaptation
Table measures CiL performance in scenario with task-level unlearning. For comparison, we use an adapter-based approach with parameter isolation- based continual learning private unlearning (CLPU) 3. extending TAIL to TAIL-7 CLPU and IsCiL without skill adapter initialization. Similar to IsCiL, CLPU learns tasks in isolated models tagged with specific task identifiers and handles unlearning requests by removing the corresponding model parameters of the target task. Both TAIL-T CLPU and IsCiL ensure output distribution equality between the unlearned model and the model trained with the retained dataset. Thus, their CiL performance remains largely unaffected by unleamiing.
Although IsCiL exhibits slight performance degradation of 1.8% 5.2% after unlearning, as reported in Table [ it still demonstrates robustness by achieving 115% higher AUC compared to TAIL-7 CLPU in incomplete scenarios.
4.6 Analysis
Rehearsal comparison. Figure 4 compares the sample efficiency to retain learned knowledge between IsCiL and rehearsal-basee -based continual imitation learning approach, Experience Replay (ER) [40] For ER, we adjust the number of stored samples per learning stage, while IsCiL does not store rehearsals
8

---

Evolving Complette
Evolving Kitchen- Semii
Evolving Kitchon-Inooo Incomppllee
60
Rehearsalsttage
Rehearsa/stage
Rehearsal/stap
LCT ma 1sCIL-AUC
ER-FWT
ER-AUC
Figure 4: Comparison w.r.t. the number of rehearsals: The horizontal axis represents the amount of stored rehearsal data each stage, while the vertical axis indicates goal-conditioned success rates (GC).
Evolving world-Compled
TAllot
LONA
15
10%
Training Epochs
20%
50%
IsC1L
TAIL-r
TAIL-g
L2M-g
L2M
FWT
AUC
Figure 5: Comparison w.r.t training resources: In baselines, the plain graph represents FWT, while the bar graph with hatch marks represents AUC. The vertical axis indicates goal-conditioned success rates (GC).
for training. IsCiL achieves the highest AUC in all environments and is the only approach where AUC surpasses FWT. ER shows comparable FWT in Complete, but as the number stored samples increases, FWT decreases, indicating that more rehearsals actually reduce training sample efficiency. In Semi and Incomplete, using 250 rehearsals (approximately 5% the stage dataset) yields FWT comparable IsCiL but rarely improves AUC.
Limited training resource. Figure shows the computational efficiency of IsCiL in resource- constrained training settings, as discussed in (38). In this experiment, the training resources is limited to 1% to 50% of those used in Table T IsCiL and TAIL-F show robust performance for varied training resources. TAIL-g shows higher FWT. it trains the same sub-goat data the same adapter, which excels in learning new tasks, but fails to retain that knowledge. However, using skill data from different stages to update the adapter makes it vulnerable skill distribution shifts in CiL.; this ends up with significant AUC degradation.
4.7 Ablation
Table 4 investigates the impact of the num- ber of prototype bases on CiL performance, showing that increasing the number of bases improves both AUC and result stability, par- reported
Table 4: Ablation on IsCiL skill prototype
Stream
Evolving kitchen-Commn CocclenCCmmplte
Ablations
FWT (8)
BWT (8)
AUC (8)
ticularly around K=10. Results based on units (g and 7) used to construct new skill prototypes and the corresponding number of bases. IsCiL with a single base fails effectively learn task knowledge, achieving similar performance to L.2M in TT, due to insufficient representation of the skill distribution. Additionally, the IsCiL framework maintains positive BWT scores, demonstrating its ability to leverage future samples enhance past performance. IsCiL with which constructs new skills based entire task trajectories, required more bases in proportion the increase in the number of transitions involved in constructing the skill trajectory to maintain stability.
IsCil 1xal
20
79.3
11.0=16
89.8
IsCiL g. Xx
28.9
2.3+57
30.6-122
IsCiL 9. Xx
63.1
2.7+73
66.5=79
IsCiL, 9- X=
76.4265
8.2+40
83.9
IsCiL 9. X=
77.1
11.9
88.2-111
IsCiL g. Xx
81.5
7.9449
89.4=11
IsCiL
20
57.8+166
10.9
67.2-172
IsCiL
80
84.3
5.0.75
89,5

---

5 Conclusion
In this study, we presented the IsCiL framework to address key challenges in continual imitation learning (CiL). Our approach incorporates adapter- based skill learning, leveraging multifaceted skill prototypes and an adapter pool to effectively capture the distribution of skills for continual task adaptation. IsCiL specifies enhanced sample efficiency and robust task adaptation, effectively bridging the gap between adapter-based CiL approaches and the need for knowledge sharing across staged demonstrations. Comprehensive experiments demonstrate that IsCiL consistently outperforms other adapter-based continual learning approaches in various CiL scenarios.
Limitations. Like other adapter-based CiL approaches, IsCiL. requires extra computation for evalua- tion, which can create overhead, especially in resource-constrained environments. It also depends on sub-goal sequences for training and evaluation, adding complexity and resource demands. An- other limitation is determining the appropriate size of the adapter parameters, which depends on the performance of the pre-trained base model and the degree of task shift, making optimal adaptation challenging. Moreover, balancing the stability of the embedding function with the prototype size remains an area that requires further refinement achieve optimal performance.
Acknowledgement
This work was supported by Institute of Information & communications Technology Planning & Evaluation (ITP) grant funded by the Korea government (MSIT) RS-2022-11220033 (2022-0- 00043), Adaptive Personality for Intelligent Agents, multi-modal Intelligence for solving unknown, open domain problems, Intelligence Graduate School Program (Sungkyunkwan University), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-002211188 and by Samsung Electronics.
(2022-0-01045), Self-directed
Artificial
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A Environment and Data Stream Details
A.1 Franka Kitchen
We conduct experiments on the Franka kitchen environment 22 30
Each Franka kitchen task com- prises of 4 sub-goals, from total pool 7: microwave, kettle, bottom burner, top burner, light switch, slide cabinet, and hinge cabinet. Observation is 60-dimensional vector, which is combination of the positions and velocities of 7-DoF robot arm and interacting objects. We express sub- uub-goal information using language embedding. The target sub-goal of the current state is acquired by pre-defined environmental reward and task, and a sub-goal sequence to solve. We use 24 tasks in the 'mixed' dataset from the D4RL [29]. In the pre-training stage, train the model only tasks comprised of following four sub-goals: kettle, bottom burner, top burner, light switch.
Pre-training stage
Continual imitation learning stages
Figure 6: Example of a multi-stage Meta-World environment in our continual imitation learning
scenarios.
A.2 Multi-stage Meta World
We conduct experiments on the multi-stage variation of the Mera-World environment [31 Each Meta-World task comprises of 4 sub-goals from total pool of 8: puck, box, handle, drawer, lever, button, door, and stick. The environments are divided into different scenarios based on which out of 8 objects are placed on the table. For each environment, tasks are defined according to the sequence which the sub-goals must be achieved. Observation a 140-dimensional vector, which contains the positions and velocities of the 4-DoF robot arm and all interacting objects in the environment. In this environment, sub-got goal information also expressed using language embedding. The expert dataset is collected using heuristic expert policy provided by Meta-Worid [3I). In the pre-training stage of Meta World, we train the model on 24 tasks on a environment consisting of four objects: a puck, a drawer, button, and a door.
A.3 Data Stream
Evolving Kitchen Tables 5 and display the detailed configurations of the Evolving Kitchen in our CiL scenario. Each task involves sequentially solving its respective sub-goals. The underlined sub-goals (c.g., kettle) are those missing in the Semi Complete and Incomplete scenarios.
Evolving World Tables and 8 display the detailed configurations of our Evolving World CiL scenario. Similarly, the Evolving World is also presented the same way as the Evolving Kitchen.
Unseen Task Adaptation Tables 9 and 10 show the detailed configurations of unseen tasks for our Evolving Kitchen-Complete Unseen and Evolving Norld-Complete Unseen. In the Evolving Kitchen, 2 new tasks appear every 5 stages. In the Evolving World, 4 new tasks appear every 4 stages. Each new task includes only the sequence of sub-goals that must be completed in order, without any demonstrations.
13

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Table 5: Evolving Kitchen- Complete & Incom- plete data stream task configuration
Table 6: Evolving Kitchen-Semi data stream task configuration
Evolving Kitchen- Complete & Incomplete
Evolving Kitchen-Semm
Task
Sub-goal 1
Sub-goal
Sub-goal
Sub-goal
Task
Sub-goal
Sub-goal
Sub-goal
Sub-goal
71
microwave
kettle
top
light switch
microwave
top
light
72
kettle
bottom burner
top burner
slide cabinet
kettle
bettom burner
top burner
slido cabinet
microwave
botiom burner
burner
slide cabinet
microwave
botiom burner
top
slide cabinet
74
kettle
botiom burner
light switch
slide cabinet
74
kettle
botiom burner
light switch
slide cabinet
microwave
kettle
light switch
slide cabinet
microwave
kettle
light switch
slide cabinet
To
bottorn burner
top hurner
hinge
bottom hurner
top
hinge cahiner
microwave
kettle
burner
binge cabinet
microwave
kettle
top burner
lange cabinet
kettle
siide cabinet
hinge cabinet
kettle
slide cahinet
hinge cahinet
To
kettle
light switch
slide cabinet
hinge cabinct
kettle
light switch
slide cabinet
hinge cabinct
microwave
kettle
bottom bumer
cabingt
microwave
kottle
bottom buner
binge cabinet
T1
kettle
bottor hurner
cahinet
hinge
71
microwave
top
light switch
kettle
bottom burner
light switch
binge cabinat
kettle
bottom burner
top burner
slide cabinet
top burner
light switch
binge cabinet
microwave
botiom burner
burner
slide cabinet
714
micromave
kettle
bothom bumer
slide cabinei
TL4
light switch
slide
microwave
kettle
light switch
hinge cabinet
m. microwave
kettle
light switch
slide cabinet
716
microwave
bottorn burner
top hurner
light
kettle
bottom burner
top
binge cabinet
kettle
top burner
light switch
slide cabinct
717
microwave
kettle
top burner
hinge cabinet
microwave
botiom burner
burner
cabinet
microwave
kettle
slide cabinet
hinge cabinet
71
microwave
bottorn
cabinet
hinge
719
light kettle
cabinet
hinge cabinet
720
microwave
bottom burner
light switch
slide cabinct
microwave
bottom bumer
hinge cabinet
Table 7: Evolving World-Complete & Incomplete data stream task configuration
Table 8: Evolving World-Semi data stream task configuration
Evolving Worid-Complete & Incomplete
Evolving World-Semi
Task
Sub-goal 1
Sub-goal
Sub-goal 3
Sub-goal 4
Task
Sub-goal
Sub-goal 2
Sub-goat
Sub-goal 4
door
handle
button
box
door
handle
button
box
puck
drawer
stick
lever
drawer
stick
lever
bandle
puck
lever
door
handle
puck
lever
door
button
drawer
box
stick
button
drawer
box
stick
door
handle
button
door
handle
box
button
lever
stick
drawer
puck
lever
stick
drawer
puck
77
lever
puck
handle
door
lever
puck
handle
door
stick
button
drawer
box
stick
button
drawer
box
handle
button
hox
door
To
handle
button
box
door
drawer
stick
lever puck T1o
drawer
stick
lever
puck
puck
lever
door
handle
door
bandle
button
box
stick
button
box
drawer
T12
puck
drawer
stick lever
handle
button
door
box
715
bandle puck
lever
drawer
lever
stick
puck
button
drawer
box
stick
T15
puck
lever
handle
door
T15
handle
button
stick box
button drawer
lever
stick
drawer
puck
T17
handle door
box
button
T17
lever
pack
handle door
stick drawer
lever
Tis
button
drawer
box
door pack
lever
handle
handle
button
box
door
box
drawer
button
stick
Tao
drawer
stick
lever
puck
Unlearning Scenario In the Unlearning Scenario, learned task is unlearned every 5 learning stages. In the Evolving Kitchen Unlearning scenario, 74, 78, T13, and T17 sequentially unlearned.
B Experiment Details
B.1 IsCiL Implementation
IsCiL consists of two modules: a skill retriever, THe and a skill decoder, TD. The skill retriever R includes three components: a state encoder f. skill prototypes X, and skill adapter mapping function h. Each skill prototype X= in X' is composed of 20 bases b. To modulate skill decoder n, we use Low Rank Adaptation(LoRA. 277. In our experiment, we used 4-rank LoRA adapters for skill adapter. IsCiL training and evaluation process follows:
14

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Table 10: Evolving World-Complete Unseen task configuration
Evolving World-Complete Unseen
Table 9: Evolving Kitchen-Complete Unseen task configuration
Stage
Sub-goaa
Sub-goal
Sub-goal
Sub-goal 4
door
handle
4
pack
drawer
stick
lever
handle
puck
lever
door
4
button drawer
box
stick
Evolving Vorid-Compllte Unseen
8
door
handle
box
button
Stage
Sub-goal
Sub-goal
Sub-goal
Sub-goal
pack
drawer
microwave
kettle
top burner
slide cabiintt
handle lever
puck
door
kettle
top burner
top
box
drawer
stick
button
10
microwave
kottle
top burner
light switch
box
handle
duor
kettle
bottom hurner
top bumnrr
light switch
12
lever
drawer stick
puck
kettle
top burner
slide cubinet
hinge cabinet
handle lever
puck
door
15
microwave
top humar
slide cabinet
hinge cahiner
12
box drawer
stick
button
top
light switch
cabinet
16
door
handle
box
button
20
kottle
top buroer
light switch
hinge cabinet
pack
16
handle puck
lever
door
box drawer stick
button
20
door
bandle box
button
20
lever drawer stick
puck
20
door handle lever
puck
20
box drawer stick
button
Algorithm IsCiL Skill Incremental Learning
State encoding function Skill retriever πR
2: Skill decoder n, Pre-trained parameter Opee
3: Skill adapter mapping function h
4: for each stage in CiL Stages do
5: for each sub-goal 9 in task dataset D; do
6:
D + ((o E D; gf= =g} // filter transitions related to the current sub-goal
7:
(0t,9t) E D encode states from the filtered dataset into state 5 (f(o,
embeddings
8:
X29 -- fargmaxx.ee X.EX 8(),,8t) st E ST) retrieve the most relevant skill prototypes for
each state st
9:
Xs + Mode(X(9) // select the most frequently retrieved skill prototype from the set 0. h(x=) // map the selected skill prototype Xs to its skill adapter via h Update 0z. using Eq. (3) // update the skill adapter based on task-specific learning X UUX= X. // append the new skill prototype the skill set for future retrieval
10:
11:
12:
13:
Update the mapping function to map Xx* to the updated adapter 0z. // update h with the new skill adapter
14:
end for
15:
end for
Algorithm 2 IsCiL Evaluation
1: State encoding function f. Skill retriever πи
2: Skill decoder dp Pre-trained parameter Ope
3: while not done do
4: St = f(r, gt) // encode state
5: 0₂ (R(St) // retrieve skill
6: à (o 9s: p 0z) // decode the skill
7: end while
15

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B.2 Baselines
Seq-FT & Seq-LoRA Sequential Fine Tuning(Seq-FI) is a method that updates the entire model sequentially. The variation, Seq-LoRA, is used to determine how effectively the fixed pre-traine pre-trained model can utilize its knowledge. Due to poor performance at very low ranks, Seq-LoRA was trained with 64-rank adapter in our environment.
4 Elastic Weight Consolidation (EWC) regularizes the weight update by using the Fisher information matrix for each network parameter. For our experiment, we adopted the online version of EWC, which updates the Fisher information at each stage by exponential moving average, following the methods in [38, 7
We use the hyperparameter alpha, set to 10, to determine the regularization strength. For updating the online Fisher information matrix F;, we use the Fisher information matrix calculated at the current stage and apply the following formula for regularization: F, y
(1 - F,, where y is set to 0.9.
6 L2M is an adapter-based continual learning method consisting of keys and their corre- sponding adapters. When an input provided, 1.2M uses a similarity function to search for the key corresponding to that input. Input is converted to query and utilized to search for a key, where key a vector with the same shape as the query. Each key then updated to maximize its similarity to the data point associated with it. Finally, the data is used to update the adapter that corresponds to the key value found through that data. This method maximizes the diversity of key usage frequency by adjusting the similarity between keys and input values during the training phase for learning new tasks [18 6]. In our implementation, we use the normalized state value as the input query to find the key in L2M. For L2M-g. we use the normalized embedding of the state concatenated with the given conditioned sub-goal information directly as a query. Our adapter pool consists of 100 adapters, cach being a 4-rank LoRA adapter.
TAIL 7
TAIL directly assigns an adapter to the given task using the task's identifier. We directly map the given identifier to the corresponding adapter. TAIL-g uses 4-rank adapter, while TAIL-T uses a 16-rank adapter.
Multi-task At each stage, the model learns from the given data and stores all data for the next stage. The data stored in the buffer is mixed with the data from each stage in a 1:1 ratio for training the model.
40 Experience Replay (ER), similarly, retains knowledge by storing subset of the current stage's 's data for the next stage. The data stored in the buffer is mixed with the data from each stage in 1:1 ratio for training the model.
CLPUB] Continual Learning Private Unlearning (CLPU) 3 is a method for managing continual learning and unlearning. In continual learning scenario, tasks that require maintenance are trained using existing models, while data that may require unlearning is trained on independent model parameters, tagged with when and through which task each model was trained. When an unlearning request for a specific task or training stage is received, the corresponding model parameters are completely removed to climinate the influence of the target unlearning task from the model. CLPU provides highly efficient and powerful unlearning performance with a single delete operation for tasks learned in continual learning scenario. In our experiment, CLPU with TAIL-T, which conducts training through task information- based searches, to handle unlearning requests in continual imitation learning comparative method.
integrate the unlearning approach
B.3 Metric
We report 3 metrics for CiL performance for tasks: Forward Transfer(PWTT), Backward Trans- fer(BWT), Area Under Curve(AUC) [38] 77. In multi-stage stage environment task, we report performance using the goal-condi success rates (GC), which evaluate the average success rate of successfully completed sub-goals out of N sub-goals in the task.
FWT: FWT, = ІT_ Eiel, Cr.i where 7 is task and Cr,s represents the GC score of task T at stage i. and 1, is set of stage indices where task T is trained in the CiL scenario.
BWT: BWT, 2.1 Eier, (pinn E) Cr.)). where p is the final stage at which task is available. In the case where p i BWT is 0.
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AUC: AUC, T. (+
Crs). represent the the overall performance of continual learning, internally including FWT and BWT. In the case where i, AUC, is FWT,.
The final reported metric is the average across all tasks T. For all metrics, higher values indicate better performance.
B.4 Scenario training details
Pre-trained Base Model and Stage Settings showws of the model we used as the base model for all baselines. T2 shows the common hyperparameters used to train the model for each stage in our experiments.
shows the hyperparameters and the architecture
Table 1 I: Pre-trained model configure
Hyperparameter
Value
Table 12: Continual imitation learning default hyperparameters
Diffusion Model
DDPM [36
Denoising step
128
Schedule
Linear
Hyperparameter
Value
Linear start
le-4
I Learning rate
Se-4
Linear end
2e-2
Optimizer
Adam
Block
MLP
Epochs/stage
5000
The number layers
6
hidden dimension
512
Layer normalization
yes
Pre-trained model performance Table 13 shows the learning performance of the pre-trained model and its adaptation performance for tasks learned in scenarios without any prior training. In Evolving World, where new objects are added and the environment changes significantly, the pre-trained model failed to successfully complete any sub-goals of tasks.
Table 13: Pre-trained model performance
Streami Total phuse)
Evolving
Evelving World- Coni--Comeete
Evolving World-Semi
Evolving Indddleetepetee
Evolving Kitchen Pre truined
98.8
21.3
29.
24.3 nu
Streami Total phase)
Evolving
Evelving Kitchen- Caabbnn-Complte
Evolving Kitchea-Semi
Evolving Kitchen decompteee
Evolving World Pre-trained
100.0ena
0.0:00
0.0ana
0.0
B.5 Compute Resources
Computing machine Our experimental platform is powered by an AMD 5975wx CPU and 2x RTX 4090 GPUs. The operating system used is Ubuntu 22.04.4 LTS, with Nvidia driver version 535.171.04 and CUDA version 12.2.
Software Detail We utilized jax 0.4.24, jaxlib 0.4.19, and flax 0.8.2 for our implementation.
Training time In the context of the Evolving Kitchen, each scenario involves training with three different seeds. The training duration averages minutes per stage, with each stage consisting of 5000 epochs. Each scenario comprises 20 such stages, culminating total training time of hours for single experiment.
C Additional Experiments
C.1 Main experiment extension
Training curve. Figure 7 shows training curves of Evolving-kitchen Complete and Incomplete on T The curves provide clear illustration of the performance progression IsCiL and baseline methods, making changes in key metrics over the course of training casily observable.
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C.2 Analysis
Skill adapter rank. Table 14 shows the results of the ablation study the performance of CiL based on the rank of the skill adapter. Overall, the 1-rank adapter in Evolving Kitchen demonstrates sufficient, even superior, adaptation performance. However, in Evolving World, the 1-rank adapter leads to lower overall performance, indicating that some skills cannot be fully learned with a I 1-rank adapter, resulting in decline in performance.
Skill decoder pre-trained model quality. Table 15 shows the results the ablation study on performance changes based on the quality of the pre-trained model (skill decoder). The quality of the pre-trained model varies with the number of objects included in the tasks used to pre- pre-train the model. A decrease in the quality of the pre-trained model leads performance drop in both TAIL-r and IsCiL, as the number of objects is reduced from to
Scenario task sequence variation. Table shows the results of the task sequence variation analysis. We report the average performance for four different task sequences in Evolving Kitchen-Comple The performance of all tasks at the final stage is not significantly affected. Since TAIL-T learns independently for each task ID, there was no performance change with different sequences, and IsCil. also showed similar performance, indicating that task sequence variation had minimal impact on overall outcomes.
Computational efficiency. In our framework, skill retrieval and adaptation occur at each time step. Despite this continuous process, the impact inference time and computational demands is minimal. Through our implementation on JAX, we observed that factors like compile optimization had more significant effect on performance than model size. As result, IsCiL demonstrates fast evaluation times, with retrieval and adaptation processes taking 3.6ms and 3. 3.0ms, respectively, which ensures that IsCil. remains highly efficient during inference.
Additionally, the memory overhead required for the adapted model is minimal, with the skill adapta- tion adding only 0.37% to 1,48% additional parameters compared to the pre-trained model, depending on the LoRA rank (1 to 4). For skill retrieval, the parameter size of each skill prototype is relatively small, accounting for approximately 0.3% of the total model size. Furthermore, the inclusion of adapters in the skill decoder only increases the FLOPs by 3.13% of the pre-trained model, demon- strating that the retrieval and adaptation processes are computationally efficient and have negligible impact on resource consumption.
Table 14: Ablation study on the skill adapter rank in Evolving Kitchen- Complete and Evolving World-Complete.
Stream
Evolving Kitchen- Kitchen-Comple Complete
Evolving World-Complete
Rank
CiL-algorithm
FWT (%)
BWT (%)
AUC (%)
FWT (%)
BWT (8)
AUC (5)
L2M-g
30,242.1
2,6=100
33.0=16
56.843.
-16,9=32
41.6:11
TAIL-g
93.2
16..3
45.7 11
77.0
-47.9
34.6.1.7
IsCiL
89.2
2.7110
91.6
73.6
-3.3
70.9 13.
L2M-g
38.24:3
-6.5
32.3
64.2433
-19.3=44
48.8 48.6#22
TAIL-g
85.3+8.0
-49,9267
41.5=17
90.0+3.0
-56.8-04
39,5+22
IsCiL
79.3+17
11.0#1.
89.8
81.7-10.4
2.7-09
84.3
18

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Cumulative task success rate (Evolving Kitchen complete)
IsCIL
TAIL-r
Stage
Cumulative task success rate (Evolving K Kitchen incomplete)
IsCiL
-
Stage
IsCiL
TAIL-r
TAIL-g
L2M-g
L2M
Seq
EWC
Figure 7: Evolving Kitchen-comple and Evolving Kitchen ietteenn-imcmplete training curves represent the cumulative task success rate up given stage. The goal conditioned success rate(GC) is scaled such that achieving success in all tasks by the final stage is represented as 100%. This result corresponds the data presented in Table T
Table 15: Ablation study on the quality of the skill decoder pre-trained model in Evolving Kitchen- Complete and Incomplete.
Stream
Evolving Kitchen- Kitchen-Complete
Evolving Maachen-Incomplets
CiL-algorithm
Pre- tre-training
FWT (8)
BWT (%)
AUC (%)
FWT (5)
BWT (5)
AUC (%)
object
72.8+79
0.0.000
72.8
28.8
0.0
28.8
TAIL-7
2 object
87.2
0.0
87.2
35,9=26
0,0
35.9
4 object
86.2+
0.0
86.2
33.8#30
0.0=0.0
33.8
object
60.0+:4.
2.1+44
62.1 =0.1
42.1
5.4
47.0+
IsCiL
2 object
78.9+5.1
6.4
84,9=1.3
56.7
12.0+23
67.3
4 object
79.3
11.021.
89.8
61.8
13.7429
14.0
Table 16: Analysis of task sequence variation in the CiL scenario of Evolving Kitchen- SoichenCComplet...
Stream
Evolving Kitchen-Complete
CiL-algorithm
Task sequence
FWT (%)
BWT (s)
AUC (%)
Seq.
79.3
11.0
89.8
Seq.
80.4
4.4+27
17.6
IsCiL
Seq. 3
63.5=3.1
13.0
76.0
Seq.
89.6
1.3
90.8
IsCiL
Average
78.2+10.0
7.4
86. 16.6
TAIL-r
Average
86.2=566
0.0++0.
86.2+:5.6
19

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C.3 Scalability
LIBERO. [ a comprehensive visualization of the Skill Retriever in the LIBERO-gon -goal scenario. The visualization highlights how the retriever successfully identifies and shares skills across different stages of CiL. This demonstrates its adaptability handling varied states and tasks, showing its potential effectiveness even in complex LIBERO environments.
Training dataset. (LIBERR--oal)
Retrieved skills (IsCiL)
(a)
(a)
Skill retrieval accuracy
50
-32
100
-002
Leomed slage retreved skills
Stage
Figure Visualization Skill Retriever on the LIBERO-goal Scenario. Left: T-SNE visualization of the state space of the existing dataset for each stage. Middle: Visualization of the stages where skills retrieved by the Skill Retriever, after all CiL stages of learning. Right: Map showing the stages of each dataset and the retrieved skills. This demonstrates that the Skill Retriever can find skills capable of handling the given state, even in the LIBERO scenario. Additionally, in task-speciff parts (a), accurately retrieves the skills, and in parts showing similar behaviors (b), shares skills.
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Reviews:
[{"review_id": "oXhxuoIJFM", "reviewer": "Reviewer_KNda", "summary": "Learn a two-layer hierarchy from a sequence of datasets, where the low-level skills are represented by a discrete set of prototypes: vectors that can be mapped to repeated patterns of actions represented by basis functions. The basis function parameters are then passed into a decoder function which takes actions based on the observation and goal. The prototypes are recovered by performing k-means to cluster the data of a particular skill. The frequency of skill selection overall is a score, and the decoder is trained with imitation learning.", "questions": "Why is Continual Imitation Learning abbreviated CiL? It seems like it should be CIL.\n\nAre there clear ablations on how the many components contribute to the overall performance?\n\nWhat metric can be used to evaluate the unlearning capability in the context of privacy? Can this be used to ensure particular data is not used? Was this evaluated?", "limitations": "see above.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "Introduces a complex but clearly effective system for skill learning.\n\nShows good results in an important setting of imitation learning from multiple datasets.\n\nDemonstrates unlearning capability, which is useful in some contexts.", "weaknesses": "It is not obvious how the skills might be entangled together post-hoc, since the reusability of a skill across tasks might make its unlearning impossible. The experimental results also seem cherry-picked to ensure that this is not an issue, which is probably disingenuous to the actual cause of privacy: whether a component be relearned without any of the information from a particular source.\n\nThe experiments appear to be convincing only in the semi and incomplete settings, but it is not entirely clear what the semi or incomplete settings are. Without a clear picture of how these components are defined, it is not clear whether the empirical results actually support the claims made in the introduction.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thorough and insightful review of our paper. Here, we respond to your comments and address the issues.\n> W1. How the skills might be entangled together post-hoc? since the reusability of a skill across tasks might make its unlearning impossible, the experimental results also seem cherry-picked to ensure that this is not an issue, which is probably disingenuous to the **actual cause of privacy**: whether a component be relearned without any of the information from a particular source.   \n\nHere, we provide the detailed process by which skills are connected post-hoc.\n\n* IsCiL incrementally accumulates skills by saving the adapter and prototype pair of each skill for every Continual Imitation Learning (CiL) stage. As described in Section 3.3, once skills are learned and accumulated, the pairs remain immutable after the stage they are learned until an unlearning request is made.\n* During evaluation, the skill retriever search appropriate skill by searching a nearest neighbor of the given state. This retrievable skill accumulation allows the model to improve its performance over time. For example, even if the model initially performs poorly when encountering an unfamiliar state, it can later retrieve relevant skill data if it becomes available. By using the skill prototype, the model can infer the correct action for that state. \n\nWe emphasize that the actual cause of privacy in IsCiL is maintained at the **task level**, and this is **not a cherry-picked result for our setting**.\n\n* In Table 3, we demonstrated a task-level unlearning case. In our study, CiL, a task is a sub-goal sequence, and different sub-goal sequences represent different tasks, as described in Section 3.1. Its subsets, overlapping skills, can of course exist through other tasks. Therefore, the unlearning experiment in Section 4.5 aims to make the model behave as if the unlearning task data were never used in training. This process involves deleting the skills (adapter and prototype pairs) learned through the target unlearning task, thereby completely eliminating the impact of the task's data (particular source) had on the model.\n\n* Accordingly, like CLPU, IsCiL meets the actual cause of privacy for target task unlearning: achieving model parameter distribution equality between the unlearned model and the relearned model, which is trained with the same learning algorithm as if the particular source(target unlearning task data) never existed from the start. To ensure this equality, the initialization of the skill adapter was modified to use only the information from the pre-trained model.\n\n* For future directions, unlike our task-level unlearning, completely forgetting the target task while retaining the performance of multiple tasks strongly affected by the skills in the target task will be a very important and challenging area for unlearning.\n\n\n> W2. semi and incomplete settings is not entirely clear. How empirical results actually support the claims made in the introduction?\n\nSemi and Incomplete scenarios refer to CiL situations where comprehensive expert demonstrations are not provided (Section 4.1, Figure 3). We will add a clear explanation of these scenarios in the final version. Concise details are as follows:\n\n* Complete: Consists of 20 CiL stages, each stage incrementally introduces tasks with objects not present in the pre-training stage, along with comprehensive demonstrations.\n\n* Semi: The first 10 stages of the Complete scenario are repeated twice. Each stage includes tasks with incomplete demonstrations, where trajectories for specific sub-goals are missing.\n\n* Incomplete: All stages have the same sequence of tasks as in the Complete scenario, but each stage includes tasks with incomplete demonstrations, where trajectories for specific sub-goals are missing.\n\nFor example, in the Semi or Incomplete scenario, if a task composed of sequential sub-goals a-b-c-d is missing sub-goal b, the demonstration will reflect this omission, resulting in a sequence like a-[]-c-d, where [] indicates the missing part. Appendix A.3 provides detailed information about the task sequence and missing parts for each scenario.\n\nWe tackle sample efficiency in CiL, which does not require comprehensive demonstrations and rehearsals. Sample efficiency refers not only to the learning efficiency within the stage the sample belongs to but also to the learning and evaluation efficiency across stages. This can be verified through FWT and BWT metrics. Therefore, the high AUC (including FWT and BWT) performance in semi and incomplete scenarios, which require knowledge from other stages to address missing parts, quantitatively validates our sample efficiency.\n\n> Q1. Why is Continual Imitation Learning abbreviated CiL?    \n\nWe have opted to use 'CiL' for Continual Imitation Learning to avoid confusion, as 'CIL' is commonly used to refer to Class Incremental Learning.\n\n> Q2. Are there clear ablations on how the many components contribute to the overall performance?    \n\nThe key components of IsCiL are the skill retriever and skill decoder. The performance ablation of the skill retriever is provided in Section 4.7, while the ablation related to the skill decoder is reported in Global Rebuttal Experiments 1 and 2.\n\n> Q3. What metric can be used to evaluate the unlearning capability in the context of privacy? Can this be used to ensure particular data is not used? Was this evaluated?\n\n* In Table 3, we found it meaningless to measure unlearning capability as a metric. This is because ensuring independent adapters for each task resulted in our **unlearned model and relearned model being exactly identical when given the same seed**. Although it is possible to compare the unlearning capability of our policy using the Wasserstein Distance(WD) between the output distributions of the unlearned model and the relearned model, following [2], this comparison also becomes meaningless for the same reason.", "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": "HgbU23s1Yz", "reviewer": "Reviewer_B1dC", "summary": "The paper introduces a new adapter-based method for continual imitation learning that avoids episodic replay and exhibits better forward and backward transfer and overall performance as compared to prior work. The authors compare their method against baselines on a couple of simulation benchmarks and also provide ablation studies to motivate their design choices.", "questions": "It would be great if the authors could address the “Weaknesses” listed above. Also, is there an expanded version of the name of the method - IsCiL?\n\nI am willing to increase my score once these questions have been addressed.", "limitations": "The authors have addressed the limitations. I have suggested adding the requirement of sub-goal labeled datasets as a limitation under “Weaknesses”.", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The paper addresses an important problem of doing continual learning without having to store all the data seen in the past. \n- The method is described in detail with experiments provided on a variety of baselines. IsCIL seems to outperform baselines on both simulated benchmarks.\n- The authors include ablation studies in the paper to promote their design choices.", "weaknesses": "- The paper is a little hard to follow in certain parts. Figure 1 could be made clearer.\n- The method assumes access to datasets labeled with sub-goals. This must be added to the limitations.\n- Line 140 mentions that a fixed function f is used to encode the observation and goal into a state embedding. Is this fixed function either obtained from the pretraining phase or is a pre-trained encoder of some sort? How does this encoder deal with changes in a non-stationary environment that it might not have been trained on?\n- It might be useful to also evaluate IsCiL on the LIBERO benchmark which is developed for such continual learning studies and also provides human-collected demonstrations. This would help highlight the efficacy of the proposed method further.\n- Is the choice of using a diffusion base policy for a specific reason?\n- The exact lifelong setting for experiments in Table 1 is unclear. From what I understand, the base model is pre-trained on a subset of tasks/objects and new tasks/objects are introduced during the lifelong learning stage. Assuming the table reports multitask performance for all baselines, this raises two questions - (1) Since FWT and BWT are only reported with a single value, does this mean that the training is only done in two stages - pretraining with limited objects and all new objects introduced together? In case the new objects are introduced incrementally, should these numbers be computed for each stage where a new object/task is introduced? (2) How does varying the task order and the initial pretraining set of tasks/objects affect the final performance?\n- Lines 233-234 mention that IsCiL exhibits performance ranging between 84.5% and 97.2%. However, in Table 1, I do not see any number as high as 97% and I can see IsCiL performance as low as 68.9% in certain settings. Also, IsCiL does not seem to be “surpassing the oracle baseline for Multi-task learning” as mentioned for any of these cases. Some clarification about how to interpret these results would be helpful.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thorough and insightful review of our paper. Here, we respond to your comments and address the issues.\n> W1. Figure 1 could be made clearer.\n\nWe added revised version of Figure 1 in *global rebuttal PDF*. \n> W2. The method assumes access to datasets labeled with sub-goals. This must be added to the limitations.\n\nWe will add this limitation in the final version. However, we assert that a sub-goal labeled dataset is common for our setting of imitation learning with multiple long-horizon tasks.\nHere are the reasons for our claim:\n- To evaluate multiple tasks without additional training, the tasks must be distinguishable based on given state information.\n- This assumption is also used in previous baselines such as L2M and TAIL.\n- Particularly for long-horizon tasks, language-based sub-goal labels are commonly used for task label[10].\n\nMoreover, prototype-based skill incremental learning (Section 3.3) can be extended to cases where skills are learned without task information (sub-goals), making it task-agnostic. However, similar to skill-based reinforcement learning, it will require post-training of a high-level policy to learn task information. Addressing this assumption is essential for our future work in lifelong skill-based reinforcement learning.\n\n> W3. Is fixed function(state encoder) f either obtained from the pre-training phase or is a pre-trained encoder of some sort? How does this encoder deal with changes in a non-stationary environment that it might not have been trained on?\n\nWe use a very simple concatenation as a fixed encoding function for f. This is defined from the pre-training phase and is some sort of a pre-trained encoder[6]. The reasons for this encoder design choice and its effectiveness in handling non-stationary environments are as follows:\n\n- In adapter-based CiL, task performance depends on the accurate skill retrieval process using the encoded information.\n- The bias of a pre-trained encoder does not guarantee distinction in non-stationary environments. Retraining the encoder could negatively impact overall performance due to distribution shifts, reverting to the continual learning problem.\n- In this retrieval-based system, we can apply various algorithms to improve retrieval accuracy and speed, ensuring that IsCiL remains efficient and accurate despite the stable encoder. For example, IsCiL ensures accurate skill distribution retrieval by utilizing multifaceted bases.\n\nTherefore, IsCiL robustly handles unknown non-stationary environments using multifaceted skill prototypes, even with a fixed encoding function f.\n> W4. It might be useful to also evaluate IsCiL on the LIBERO benchmark.\n\nWe demonstrate in the *global rebuttal PDF* that IsCiL can also be applied to LIBERO benchmark.\n> W5. Is the choice of using a diffusion base policy for a specific reason?\n\nYes, the choice of using a diffusion-based policy is due to its superior performance and ability to handle multi-modal trajectory distributions, as noted in [3,4]. \n\nThe important point is that IsCiL and other adapter-based baselines are agnostic to the pre-trained model architecture. These inherent advantages of diffusion-based policies create a powerful synergy when combined with the pre-trained model agnostic approach, making IsCiL highly effective in realistic CiL scenarios where the amount of available datasets (expert demonstrations) is limited and tasks and sub-goals can be achieved through multiple paths. Consequently, there was no reason to use a different model architecture for continual imitation learning.\n> W6. The exact lifelong setting for experiments in Table 1 is unclear. (1) the training is only done in two stages? (2) How does varying the task order and the initial pretraining set of tasks/objects affect the final performance?\n\n(1) Our experiment scenario is divided into two parts: Pretraining (stage 0 for convenience) and The CiL scenario(stages 1 to 20). Each CiL stage introduces new tasks, including unseen objects in the pretraining phase. Details of the pretraining tasks and CiL stages are provided in Appendix A.3.\n\nOur metrics are calculated after all CiL stages (20) have been completed. We recorded the success rates of all learned tasks. For each stage (1-20), we calculate FWT, BWT, and AUC. The final reported scores are the averages of all learned tasks(Appendix B.3).\n\nThe oracle baseline (multi-task), as described in Section 4, refers to the scenario where all data from each of the 20 stages is stored and used for rehearsal in subsequent stages, allowing the model to be fully trained with this cumulative data. Similarly, we recorded the success rate for each stage's task and reported the numbers similarly to other baselines.\n\nFor intuitive understanding, we present the learning curve of Table 1 showing the comprehensive task performance at each stage in the *global rebuttal PDF*.\n\n(2) We added the CiL performance on pre-trained model's quality and task order variations on *global rebuttal Exp 2,3*. The experiments were conducted under the same conditions as Table 1.\n* In Exp 2, the lower the quality of the pre-trained model, the more degradation occurs due to the capability limits of the adapter.\n* In Exp 3, the performance of all tasks at the final stage is not significantly affected. However, the FWT, BWT, and AUC reported in the paper are affected because these scores reflect the interaction of knowledge between tasks throughout the entire scenario, which is influenced by the order.\n\n> W7 Confusing explanation in Lines 233-234.\n\nThank you for pointing out the ambiguity and the incorrect expression! We intended this sentence to emphasize that our performance achieved a final AUC of 80\\% to 97.2\\% of the oracle baseline's performance. We will update this in the final version.\n> Q1. Is there an expanded version of the name of the method - IsCiL?\n\nYes, Incremental skills for Continual Imitation Learning(IsCiL) is the expanded version.", "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": "paXQ3vyKC2", "reviewer": "Reviewer_kNto", "summary": "The paper presents IsCiL, an approach to continual learning that addresses the limitations of knowledge sharing in traditional Continual Imitation Learning methods. IsCiL uses a prototype-based skill incremental method where each skill is represented by prototype embeddings and skill adapter parameters for LoRA adaptation. The state encoder encodes observations and subgoals into state embeddings, while the skill retriever matches these to existing skill prototypes during inference. The method is evaluated in environments like Franka-Kitchen and Meta-World, demonstrating its ability to learn and adapt without needing complete expert demonstrations. The results show IsCiL's performance in task adaptation and its ability to perform task unlearning for privacy concerns.", "questions": "1. Does retrieval and adaptation occur at every time step? How does this affect inference time and compute demands?\n2. The method seems to require manual selection of the task identifier for task unlearning. How does the method handle conflicting skills that need to override or combine with existing prototypes? How are skills consolidated as the number and complexity of skills learns grows? \n3. Can the unlearning process be applied at different granularities (e.g., specific sub-tasks or stages within a task), or is it only applicable at the (sub-)task level?", "limitations": "The authors do address limitations but could include more transparency on the inference time and resource use, which the paper does not thoroughly analyze. Secondly, handling a large amount of skill prototypes without additional consolidation.", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper introduces a novel prototype-based skill retrieval mechanism that effectively learns skill prototypes and adapters. The proposed method is evaluated in environments like Franka-Kitchen and Meta-World, showing improvements over TAIL when knowledge sharing across demonstrations is important (semi and incomplete settings). While skill adapters and prototypes themselves are not particularly novel, the authors' contribution lies in the innovative method of continual learning through on-the-fly adapter retrieval. The authors additionally show that the method can enable skill unlearning without significant degradation in performance. The paper is well-structured, with clear explanations of the state encoder, skill retriever, and skill decoder components.", "weaknesses": "1. The retrieval and adaptation processes at every time step might lead to increased inference time and resources, which is not thoroughly analyzed in the paper.\n2. Handling overlapping skills appears to not be handled or need manual intervention to unlearn, which is not ideal for maintaining performance across an increasing number of tasks.\n3. The paper lacks a detailed analysis of the computational overheads and scalability issues associated with maintaining a prototype-based memory and multiple adapters, which is important for real-world applications.\n4. A major assumption in the paper is the high costs and inefficiencies associated with comprehensive expert demonstrations. It is not well motivated why obtaining a single comprehensive demonstration would be more cost-effective than multiple incomplete demonstrations. This should be further motivated.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thorough and insightful review of our paper. We greatly appreciate your constructive feedback and for highlighting our contributions! Here, we respond to your comments and address the issues.\n> W1, Q1. The retrieval and adaptation processes at every time step might lead to increased inference time and resources. Does retrieval and adaptation occur at every time step? How does this affect inference time and compute demands? \n\nThe retrieval and adaptation processes occur at each step and have a negligible impact on inference time. The IsCiL evaluation times for the pre-trained model (retrieval and adaptation) are 3.6ms and 3.0ms, respectively, making IsCiL even faster. This is because, in our implementation on JAX, factors such as compile optimization had a greater impact than the model size.\n\nThe memory required for the adapted model during inference demands only an additional 0.37\\% to 1.48\\% parameters compared to the pre-trained model, depending on the LoRA rank (1 to 4). For skill retrieval, each skill requires parameters amounting to (572; total state dimension) * (20; bases size), which is about 0.3\\% of our model size. \n\nThe computation of adding adapters to the skill decoder increases the FLOPs by 3.13\\% of the pre-trained model.\n\n> W2, Q2. How does the method handle conflicting skills and consolidate them as the number and complexity of learned skills grow?\n\nIsCiL incrementally accumulates skills by saving the adapter and prototype pair of each skill for every Continual Imitation Learning (CiL) stage, continuously accumulating overlapping skills as well(Section 3.3). Additionally, learned skills remain immutable after they are initialized and learned until an unlearning request is made.\n\nThe reasons for IsCiL adopting append-only approach are as follows:\n- Even with multiple overlapping skills, the skill retriever can find the appropriate skill through a nearest neighbor search of the encoded state.\n- Continuously appended skills make it easier and simpler to manage privacy issues immediately. While consolidating skill adapters could improve scalability, this approach might require more complex processes or additional storage space for backups when removing specific knowledge [1].\n\nIn general, as the number of skills increases, the complexity of the search also increases linearly. Skill retrieval can achieve optimized search times through GPU parallel processing and dense vector-based similarity search optimization methods [7].\n> W3. The paper lacks a detailed analysis of the computational overheads and scalability issues associated with maintaining a prototype-based memory and multiple adapters.\n\nWe analyze these issues by examining the performance changes of IsCiL based on the scalability of prototypes and adapters. Prototype scalability results are reported in Table 4, and adapter scalability according to rank is presented in *global rebuttal Exp 1*.\n> W4. A major assumption in the paper is the high costs and inefficiencies associated with comprehensive expert demonstrations. Why obtaining a single comprehensive demonstration would be more cost-effective than multiple incomplete demonstrations?\n\nOur CiL scenarios assume long-horizon and complex tasks, making it challenging to collect comprehensive demonstrations that require executing multiple actions flawlessly. However, obtaining incomplete demonstrations, where each skill is present to perform parts of these long-horizon tasks, is much easier and not limited by the length of the demonstration. This is similar to the difference between filming a one-take movie and recording short YouTube videos.\n\nTable 1’s TAIL-$\\tau$ exemplifies the issue that other CiL methodologies require comprehensive expertise, which is expensive in the long-horizon environments commonly used in real-world applications. It is difficult to combine task knowledge without forgetting when dealing with incomplete demonstrations. IsCiL ensures that even if a task initially fails, subsequent stages can reliably accumulate and share skills through retrieval, improving task success rates without additional processes. This advantage allows for easy editing and compensation for unsuccessful parts of tasks, while minimizing concerns about forgetting other skills in real-world scenarios.\n\n> Q3. Can the unlearning process be applied at different granularities (e.g., specific sub-tasks or stages within a task), or is it only applicable at the (sub-)task level?\n\nIn Section 3.4 and Table 3, we demonstrate the unlearning capability using tagged metadata(task ID by sub-goal sequence) on the skill adapter. Similar to CLPU, using an isolated nature for the target unlearning adapter(model) in a continual learning scenario provides the advantage of enabling direct and immediate unlearning through metadata tagging.\n\n**IsCiL can be applied at different granularities**. This can be achieved combining a straightforward yet practical approach of adding more metadata tags (e.g., task information, learned stage, security level) to adapters all together. This allows for finding adapters related to user requests (e.g., specific stages within a task) and using them for unlearning. Although there may be a search overhead, the unlearning process itself remains fast and minimally impacts other knowledge.\n\nFurthermore, **unlearning is also possible when the target is a skill trajectory**(in the same format used for learning) rather than tagged metadata. IsCiL allows for skill retrieval through multifaceted prototypes, enabling the retrieval of specific skills. The same method used for adapter initialization in skill incremental learning in Section 3.3 can be applied and extended for this purpose. Thus, to remove a specific skill, providing the skill trajectory allows for its removal even without tagged metadata.", "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": "3c4ddM08fP", "reviewer": "Reviewer_DhgS", "summary": "This paper tackles the problem of continual imitation learning, where an agent needs to continually adapt to new tasks through imitation learning. The paper proposed IsCiL, a method that utilizes prototype-based skill incremental learning to gradually grow a repository of skill prototypes that can be retrieved and adapted to new tasks. Evaluated on Franka-kitchen and meta world, IsCil showed superior performance over previous CiL methods.", "questions": "Although not necessary, I’d be interested to see the performance of IsCiL on more challenging benchmarks such as LIBERO. Also, given that the metrics measured by this work are intuitively visible from the training curve, it would be great to show some training curves of IsCiL and baseline methods.\n\nMinor questions:\n- How many skills are updated for each demonstration transition? It seems that only the retrieved skill is updated for each transition. Wouldn’t this be an inefficient use of data? \n- How exactly does IsCiL determine when to add new skills? Do we need a human to manually identify new skills?", "limitations": "The limitations is adequately addressed.", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The problem of continual imitation learning is very important. The overall idea of utilizing skills to achieve continual imitation learning is promising. The experimental results of IsCiL are well-presented, and thoroughly conducted.", "weaknesses": "Overall the contribution seems incremental. The idea of prototype-based skill learning, parameter-efficient adaptation via LoRA, as well as the idea of tackling lifelong learning through skills have already been explored by previous works, and it is not clear what the key innovation of this work is.\n\nSome of the important concepts in this paper, such as skill prototype, base, and skill adaptor, are not properly defined, making it hard to comprehend how they are generated and used. For example, how exactly are the pre-trained base model and the adaptor weights combined? Where does the pre-trained base model come from? How are the skill prototypes initilized?\n\nThis paper assumes access to a set of subgoals. In practice, this seems like a strong assumption, as these subgoals essentially segment the demonstration trajectories and implicitly define a set of short-horizon skills. Considering that these sub-goals are also used for generating and selecting skill prototypes, it raises the concern that these sub-goal specifications are doing the heavy lifting.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thorough and insightful review of our paper. Here, we respond to your comments and address the issues.\n> W1. The idea of tackling lifelong learning through skills have already been explored by previous works, and it is not clear what the key innovation of this work is. \n\nOur contribution lies in proposing a novel retrieval-based framework for realistic CiL (rehearsal-free, incomplete demonstration), demonstrating its skill-sharing ability with positive backward transfer and efficient knowledge management via unlearning. IsCiL is a distinct study with different problems, situations, and objectives compared to recent lifelong learning work using skills[8].\n\nHere are the differences between the latest skill-based lifelong imitation learning research and our study in terms of problem context and objectives:\n- **Existing research**[8]:\n   * Uses rehearsal\n   * Uses comprehensive demonstrations\n   * Learned skills are mutable\n   * Skills are shared by a high-level model and consolidation\n   * Avoids catastrophic forgetting\n- **IsCiL**:\n   * Rehearsal-free\n   * Uses comprehensive/incomplete demonstrations\n   * Learned skills are immutable\n   * Skills are shared through retrieval\n   * Bidirectional Skill Transfer(sample efficiency/skill sharing)\n   * Supports Task unlearning \n\nWe demonstrated through the IsCiL framework and the experiments in Section 4 that the performance of skill retrieval and the consistency of the skill adapter are crucial for CiL performance in various situations. Therefore, both studies are important as they address different key aspects of lifelong learning.\n> W2. Provide more details of framework components.\n\nFor clarity, we provide a concise overview of the overall structure of IsCiL(Section 3.3). \n- Skill Retriever: Returns the skill adapter capable of performing the given observation and subgoal(state).\n    - State encoder: Encode the given input into state.\n    - Multi-faceted prototype: Parameters representing skill precisely as bases that can be searched through the neierest neighbor by given state.\n    - Skill adapter: Adapter that can be added to the skill decoder, implemented through LoRA.\n- Skill Decoder: The model that directly infers actions from the given input. \n\nActions of Each Component Based on the Scenario follows:\nPre-training (Stage 0 or Given)\n- Pre-training: The base skill decoder is trained, and its parameters remain unchanged during the CiL stages.\n\nCiL Scenario (Stages 1-20)\n- Training (Skill incremental earning)\n    - Skill prototype initialization: Prototypes of skill obtained through centroids of KMeans.\n    - Skill adapter training: Adapters are trained using the transitions of the given skill.\n    - Append skill set: The trained skill prototype and adapter pair are added to the skill set and remain immutable in subsequent stages.\n- Evaluation\n    - Skill Retrieval: retrieve skill via given state\n    - Skill Decoding: The selected skill’s adapter combined with pre-trained model to infer actions.\n\n> W3. The assumption of access to sub-goals seems strong and raises the concern that these sub-goal specifications are doing the heavy lifting.\n\nThe subgoal assumption in rehearsal-free CiL does not directly solve the forgetting problem or significantly enhance performance. Moreover, in our work, the sequence of subgoals represents distinct tasks, which is common in long-horizon multi-task settings.\nHere are the reasons:\n- Even within the same subgoal, the distribution of skills varies at each stage. We confirm this through the performance of the Tail-g and L2M-g baselines, which use subgoal labels directly. TAIL-g trains the adapter for each subgoal but experiences overall performance degradation due to skill distribution shift and forgetting. L2M-g also shows even lower FWT than TAIL-g, as making both the adapter and retriever learnable each time makes it more unstable.\n- Long-horizon task planning often uses language-based subgoal labels. All the environments we used involve long-horizon tasks that require the sequential execution of multiple sub-tasks (Section 4.1). Including shared sub-task goals in the data helps identify the task being performed[10].\n\nTherefore, subgoal specification is not unique to our methodology. Instead, it is a general setting that underscores the importance of knowledge sharing through our skill retriever.\n> Q1. It would be great to show some training curves of IsCiL and baseline methods, as well as performance on more challenging benchmarks like LIBERO.\n\nWe added the learning curve from Table 1 and the applicability of IsCiL to the LIBERO benchmark in the *global rebuttal PDF*.\n> Q2. How many skills are updated for each demonstration transition? It seems that only the retrieved skill is updated for each transition. Wouldn’t this be an inefficient use of data?   \n\nIn our experiments, IsCiL creates 3 to 4 skills per stage, corresponding to the sub-goals in the demonstration. Updating only the skill adapter associated with a given transition in IsCiL offers several advantages:\n- It is robust to forgetting, which is critical for long-horizon tasks where a single mistake can lead to a significant performance drop. In CiL without rehearsal, if a new skill transition updates an already learned old skill, there is a risk of forgetting the updated old skill.\n- It allows for stable storage and removal of skill. Since skills are shared through retrieval, having adapters with precise knowledge related to the retrieved prototype is advantageous for skill evaluation and management.\n- It is training cost-efficient. If a single transition were to update multiple skills, the training cost would increase significantly.\n\nTherefore, IsCiL is effective in CiL scenarios without rehearsal and with incomplete expert demonstrations.\n> Q3. How exactly does IsCiL determine when to add new skills?\n\nNew skills are appended at each stage without requiring manual decisions from the user.", "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 sincerely thank all reviewers for their thoughtful reviews and greatly appreciate the insightful feedback on our work. In this section, we include experiments (with PDF) and references to address the comments provided.

## **Experiment**
---
> Exp 1. Skill adapter rank ablation. [KNda Q2 | kNto W3] 

|||Evolving|Kitchen|-complete|Evolving|World|-complete|
|-|-|-|-|-|-|-|-|
|Rank|Baselines|FWT|BWT|AUC|FWT|BWT|AUC|
|1|L2M-g|30.16|2.59|32.96|56.83|-16.93|41.60|
|1|TAIL-g|93.21|-54.30|45.68|76.95|-47.93|48.62|
|1|IsCiL|89.18|2.73|91.57|73.63|-3.31|70.91|
|4|L2M-g|38.19|-6.50|32.33|64.19|-19.34|48.62|
|4|TAIL-g|85.28|-49.90|41.54|90.02|-56.76|39.53|
|4|IsCiL|79.31|11.03|89.76|81.69|2.70|84.30|

We conduct an ablation study on the performance of Continual Imitation Learning (CiL) based on the rank of the skill adapter. Overall, the 1-rank adapter in Evolving Kitchen shows sufficient or even superior adaptation performance. In contrast, in Evolving World, the 1-rank adapter results in lower overall performance, indicating that some skills cannot be fully learned with a 1-rank adapter, leading to a decline in performance.

> Exp 2. Skill decoder pre-trained model quality ablation. [KNda Q2 | B1dC W6] 

|||Evolving|Kitchen|-complete|Evolving|Kitchen|-incomplete|
|-|-|-|-|-|-|-|-|
|Baselines|Pre-training|FWT|BWT|AUC|FWT|BWT|AUC|
|TAIL-$\tau$|1obj|72.77|0.00|72.77|28.75|0.00|28.75|
|-|2obj|87.24|0.00|87.24|35.86|0.00|35.86|
|-|4obj|86.24|0.00|86.24|33.76|0.00|33.76|
|IsCiL|1obj|60.01|2.07|62.13|42.08|5.39|46.97|
|-|2obj|78.88|6.42|84.92|56.67|11.95|67.29|
|-|4obj|79.31|11.03|89.76|61.81|13.71|74.04|

We conduct an ablation study on the performance changes based on the quality of the pre-trained model (skill decoder). The quality of the pre-trained model varies with the number of objects included in the tasks used to pre-train the model. A decrease in the quality of the pre-trained model leads to a performance drop in both TAIL-$\tau$ and IsCiL (from 4 objects to 1 object).


> Exp 3. CiL scenario task sequence variation analysis. [B1dC W6]

|4 scenario|FWT|BWT|AUC|
|-|-|-|-|
|TAIL-$\tau$|86.24|0.00|86.24|
|IsCiL|78.19|7.42|86.05|

We report the average performance for **four different task sequences** in **Evolving Kitchen-complete**. The performance of all tasks at the final stage is not significantly affected. Since TAIL-$\tau$ learns independently for each task ID, there was no performance change with different sequences, and IsCiL also showed similar performance.

## **PDF contents**
---
1. LIBERO Experiment [B1dC W5 | Dhgs Q1]
2. Revised Figure 1 [B1dC W1]
3. Learning curve of Table 1 [Dhgs Q1 | B1dC W6(1)]

## **References**
---
[1] Liu, Bo, Qiang Liu, and Peter Stone. "Continual learning and private unlearning." Conference on Lifelong Learning Agents. PMLR, 2022.

[2] Tarun, Ayush Kumar, et al. "Deep regression unlearning." International Conference on Machine Learning. PMLR, 2023.

[3] Pearce, Tim, et al. "Imitating Human Behaviour with Diffusion Models". International Conference on Learning Representations, 2023.

[4] Wang, Zhendong, et al. "Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning". International Conference on Learning Representations, 2023.

[5] Hu, Edward J., et al. "LoRA: Low-Rank Adaptation of Large Language Models". International Conference on Learning Representations, 2022.

[6] Schmied, Thomas, et al. "Learning to Modulate pre-trained Models in RL." Advances in Neural Information Processing Systems 36. 2024.

[7] Douze, Matthijs, et al. "The Faiss Library". arXiv, 2024.

[8] Wan, Weikang, et al. Lotus: Continual Imitation Learning for Robot Manipulation through Unsupervised Skill Discovery. 2024.

[9] Bruce, Jake, et al. ‘Learning About Progress From Experts’. International Conference on Learning Representations, 2023.

[10] Shridhar, Mohit, et al. "Alfred: A benchmark for interpreting grounded instructions for everyday tasks." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.

Meta-review: This submission introduces a method for continual imitation learning by prototype-based skill incremental learning. Low-level skills are represented by discrete prototypes (like basis functions), and their parameters, together with observations and goals, are passed into a decoder for action execution. The main contribution is that the proposed method eliminates the need for episodic replay, offering improved forward and backward transfer, and outperforms prior related works. The authors evaluated the method on Franka-Kitchen and Meta-World, and the method is shown to have strong adaptability to new tasks.

All reviewers agreed that the proposed method is interesting both method-wise and application-wise. The performance gain is strong as well. Thus, the meta reviewer recommends "accept" for this submission.
Decision: Accept (poster)