AutoCGP: Closed-Loop Concept-Guided Policies from Unlabeled Demonstrations

Authors: Pei Zhou, Ruizhe Liu, Qian Luo, Fan Wang, Yibing Song, Yanchao Yang
Primary area: applications to robotics, autonomy, planning
Keywords: self-supervised manipulation concept discovery, concept-guided policy for robotic tasks

Abstract:
Training embodied agents to perform complex robotic tasks presents significant challenges due to the entangled factors of task compositionality, environmental diversity, and dynamic changes. In this work, we introduce a novel imitation learning framework to train closed-loop concept-guided policies that enhance long-horizon task performance by leveraging discovered manipulation concepts. Unlike methods that rely on predefined skills and human-annotated labels, our approach allows agents to autonomously abstract manipulation concepts from their proprioceptive states, thereby alleviating misalignment due to ambiguities in human semantics and environmental complexity. Our framework comprises two primary components: an *Automatic Concept Discovery* module that identifies meaningful and consistent manipulation concepts, and a *Concept-Guided Policy Learning* module that effectively utilizes these manipulation concepts for adaptive task execution, including a *Concept Selection Transformer* for concept-based guidance and a *Concept-Guided Policy* for action prediction with the selected concepts. Experiments demonstrate that our approach significantly outperforms baseline methods across a range of tasks and environments, while showcasing emergent consistency in motion patterns associated with the discovered manipulation concepts. Codes are available at: https://github.com/PeiZhou26/AutoCGP.

Decision: accept (spotlight)
Reviewer scores: 5, 8, 8, 8

Reviews and discussion:
<thread>
creation date:
1729156611736

thread_type:
official_review

<thread_object>
creator: reviewer_umdm

<content>
summary:
The paper presents a novel imitation learning framework for training closed-loop concept-guided policies for robotic tasks, focusing on long-horizon tasks. The framework leverages self-supervised learning to autonomously discover manipulation concepts directly from robotic demonstrations, without the need for predefined skills or human-labeled data. The two main components of the system are:
- Automatic Concept Discovery, which identifies manipulation concepts by analyzing proprioceptive states and assigning discrete embeddings to different task segments.
- Concept-Guided Policy Learning, which utilizes these discovered concepts for policy learning. A Concept Selection Transformer (CST) selects relevant manipulation concepts in real time, while a Concept-Guided Policy (CGP) generates actions based on the selected concepts, using a diffusion policy to handle high-dimensional actions.

The framework is designed to dynamically adjust policies in response to environmental changes, ensuring robust performance across a variety of tasks. Experimental results show that the proposed method outperforms existing baselines in multiple robotic manipulation tasks, demonstrating improved generalization and performance in dynamic environments.

strengths:
- The approach is highly original in combining concept discovery with closed-loop policy learning. Instead of relying on predefined, human-annotated labels or skills, the system autonomously discovers meaningful manipulation concepts directly from demonstrations. This removes the need for manual intervention and avoids misalignment between human semantics and robot operations.
The use of a self-supervised concept discovery mechanism is innovative, especially in its application to long-horizon robotic tasks. The introduction of a Concept Selection Transformer (CST) for dynamic task execution is novel and enables adaptive behavior based on feedback.
- The methodology is well-designed and robust, incorporating a clear pipeline for concept discovery and policy learning. The experimental setup is thorough, with comparisons to multiple baselines and a wide range of tasks, which demonstrates the effectiveness of the proposed framework.
The paper also includes an ablation study to assess the contributions of different components within the framework, which adds depth to the analysis and validation of the proposed method.
- The paper is clearly written, with detailed explanations of the method, supported by visualizations of the discovered concepts. The distinction between the two main components (Automatic Concept Discovery and Concept-Guided Policy Learning) is clearly articulated, and the figures help in understanding the flow of the framework.
The technical details of the system, including the loss functions and architecture choices, are well-explained.

weaknesses:
- Concept Interpretability: While the system autonomously discovers manipulation concepts, it would be interesting to try to explore how interpretable or human-understandable these concepts are. It would also be interesting to see if all the discovered concepts are utilized in downstream policy learning and, if not, what the unused ones look like.

- Evaluation Scope: Although the paper evaluates the framework on a variety of tasks, the focus is primarily on tabletop manipulation in simulated environments. Expanding the evaluation to more complex morphologies or real-world scenarios would strengthen the claim that the approach generalizes well.

- Lack of Real-World Validation: While the framework performs well in simulation, it has not been validated on real-world robotic platforms. The paper acknowledges this limitation but does not provide a clear roadmap for transitioning the system to physical robots, which is crucial for demonstrating its practical applicability.

questions:
- There is some line of work using mutual information maximization to discover concepts/skills from demonstration datasets [1, 2] or self-supervised methods [3]. It may help to include some insights into those works.
- While the annotations used in the method section are very stringent and scientifically sound, it can be confusing for readers with less background knowledge or patience to fully understand the message the paper tries to deliver.
- In the automatic concept assignment stage, is the encoder trained separately from other components, or are they trained simultaneously? If the former, how did you prevent the encoder from collapsing to map to the same codebook index?
- Are all the discovered concepts meaningful and physically possible? Can I condition on an arbitrary concept index and the generated trajectories are good to use, or is the Concept Selection Transformer still necessary to act as a filter to rule out discovered but useless concepts?
- How to deal with diversity? In skill discovery works, one common issue is a diverse set of skills/concepts can be discovered, but this diversity is canceled out by a downstream skill/concept selector due to its collapse on one or two most useful skills/concepts. How can you retain all the concepts discovered and demonstrate the diversity there?

[1] Li, C., Blaes, S., Kolev, P., Vlastelica, M., Frey, J. and Martius, G., 2023, May. Versatile skill control via self-supervised adversarial imitation of unlabeled mixed motions. In 2023 IEEE international conference on robotics and automation (ICRA) (pp. 2944-2950). IEEE. 

[2] Peng, X.B., Guo, Y., Halper, L., Levine, S. and Fidler, S., 2022. Ase: Large-scale reusable adversarial skill embeddings for physically simulated characters. ACM Transactions On Graphics (TOG), 41(4), pp.1-17.

[3] Li, C., Stanger-Jones, E., Heim, S. and Kim, S., 2024. FLD: Fourier Latent Dynamics for Structured Motion Representation and Learning. arXiv preprint arXiv:2402.13820.
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</thread>
<thread>
creation date:
1729954856382

thread_type:
official_review

<thread_object>
creator: reviewer_wk2p

<content>
summary:
This paper presents a method for discovering robot actions and manipulation policies from unlabeled demonstration data. It presents techniques for discovering generic action/manipulation concepts and their goals, evaluating progress towards those goals, and generating policies to achieve the goals. This is done using various modern machine learning techniques and problem/loss function formulations. For concept discovery, A VQ-VAE is used to discover concepts and put them into a finite codebook. A transformer model is used for goal state detection. A hypernetwork and another transformer are used for goal state evaluation, and a final transformer is used for goal consolidation. All of these networks are jointly trained using a large combined loss function. Then, concept selection and policy generation are learned using a combination of a transformer and a diffusion policy. Finally, the approach is validated by training on a set of 950 robot demonstrations in 6 different tasks, then evaluated with randomized starting positions (all in simulation). The success rate of different tasks learned via the proposed method is higher than for competing metrics, even/especially in the presence of perturbing noise.

strengths:
* The paper compares the proposed approach to a set of highly relevant prior work in a fairly direct head-to-head comparison and the proposed approach nearly universally outperforms the prior work by a good margin.

* The approach is a novel combination of several modern techniques to solve a complex open problem in robotics. It expands upon work such as InfoCon by adding policy learning directly into the framework of manipulation concepts.

* The paper is written clearly and the construction of the experiments is well-explained (training, structure of network, etc.), including the re-implementation of baselines.

weaknesses:
* The primary weakness of this study is that it only contains a brief evaluation of the quality of the learned skills (i.e., are they generalized, are they more generalized than prior approaches), and a single visual example. Characterizing XSkill "concepts" via K-means clustering is perhaps not the most charitable interpretation of their concepts, as K-means clustering will naturally tend to collect noise in its clusters (noise is one of the stated drawbacks of the xskill results). Ideally there would be some sort of mean similarity score or some other comparison method - even something like a 2D embedding visualization of the different skills and concepts, if possible to generate, would give better faith in the stated quality of the learned concepts. This is a conference about learning representations, so I would love to see more focus on the quality of the representation of "concept". That being said, other papers, including InfoCon, also use final task success as the main comparison metric, so I don't consider this a big enough weakness to reject the paper.


* It is not clear why such a large variety of approaches were used in this paper. Many different transformer sizes were used, in addition to the diffusion policy and the VQ-VAE. Specifically, the use of hypernetworks also seems like an unnecessary addition to what is already a diverse and expressive set of architecture choices.

**Style notes**

* perhaps this is a notational convention I am unaware of, but the parentheses in epsilon^(n) seem like unnecessary clutter.

* Should eq 11 be negative to account for the fact that the log term will always be negative?

* In eq 10, is the selected action from pi_D conditioned on the k selected by p_CST? If so, this could be made clearer, perhaps by splitting into two equations.

questions:
* At some point, the number of different networks + their parameters runs a risk of over-parameterization and overfitting. This is especially true since it seems that the set of demonstrations were collected over the same set of tasks as used in policy generation and success measurement. Combined with the lack of qualitative cross-task concept comparison or similarity analysis, I do have overfitting worries here. I have no evidence to directly support this flaw other than my own experience training large robotics models, but would still like to know the authors' thoughts on how overfitting is avoided in the proposed approach.

* Why was a hypernetwork used for a single piece of the approach?

* How were the 950 demonstrations in the experiment generated, and were they over all 6 tasks, or a different set/subset of tasks? I was not able to find this anywhere in the paper.
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</thread>
<thread>
creation date:
1730716677856

thread_type:
official_review

<thread_object>
creator: reviewer_uouh

<content>
summary:
## Research Question
For imitation learning(IL), 1)data annotation requires additional human effort; 2) the inherent subjectivity in manual data collection\annotation leads to manipulation concepts that do not align well with the robot’s configuration, and such inconsistency will accumulate, and 3)  IL will face further difficulties due to the varying and unpredictable conditions encountered at each step. 


## Proposed Method
To mitigate the above challenges, the author proposed a framework that combines automatic concept discovery with closed-loop concept-guided policy learning. Such a framework will take unlabeled demonstration for training and autonomously extract and utilize manipulation concepts directly from the robot’s proprioceptive states.  Specifically, for training, the "Automatic Concept Discovery" module will first derive manipulation concepts automatically. Then, for both training and testing, the "Concept Selection Transformer (CST)" will propose and adjust manipulation concepts in real time during the robot’s interactions with the environment. Finally, the "Concept-Guided Policy (CGP)" utilizes the selected manipulation concepts to execute actions based on instantaneous visual input

strengths:
1. The idea is a good catch for research questions

The manual annotation(sample efficiency), accumulative action inconsistency, and environmental unpredictability are classic bottlenecks for imitation learning,  and the general idea of "self-discovered sub-policy\concepts" is also not something new. However, the author directly extracts the concepts from the robot’s proprioceptive states,  such design not only mitigates the misalignment caused by ambiguities in human semantics but also adapts dynamically to unforeseen situations. 

2. Appropriate baseline and good performance

The selected baselines are new and SOTA, and reasonable to compare with the proposed method.  Major advances of the proposed method are observed according to Table 1.

weaknesses:
1. Unclear presentation 

The figures are a little bit messy (e.g. lack the connection of each part in Fig 3, the Closed-Loop Policy part in Fig 2 is unclear  ), but it's a minor point. What matters are: 1) if the discovered concepts are consistent in all demos, i.e. if the concepts are in a unified, task-agnostic space for all demos; 2) For evaluation, it says 950 demos for each task, so does it mean: 2.1) the training data is 950 * |# of task| for single unified policy (set of concepts) and get tested all 6 type of tasks at once( in a mixed way). Or: 2.2) for each task,  it uses 950 demos to train and test only for the task.

For 1), I suppose the concepts are consistent in all demos according to the formula (1), but it's better to be clear at the beginning.  For 2), I'm not sure if it's 2.1) or not, if it's 2.2), the performance will be questionable as it's very close to overfitting. 

2. Less practical problem setting

There was a trend to label the raw robot demonstration automatically, but a more practical problem is the raw demo collection is much more expensive than demo annotation, and therefore, some researchers turn to human-play data to get more data to use. And it's the same story for this paper, the expensive robot demo collection reduces the significance of this work 

3. Concern about generalizability and scalability 

Though the paper claims better performance (compared with baselines) in a more diversified initial environment,  here the diversity is only expressed by object positions and placement angles. Some concerns are: 1)What if there is concept inconsistency in the demos,  e.g. to do the same manipulation, in a different way; 2)With the growth of the demo amount, will the # number of concepts converge or grow fast;

questions:
Dear author:

Generally, the paper is a decent work, but it needs several extra explanations and explorations.

And I will raise my rating if the following concerns are solved:
1. The concerns in the "Weakness" part.
2. A minor point is to add an IL SOTA baseline specific to the problem, which is not limited to a Diffusion Policy basis.
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</thread>
<thread>
creation date:
1730825367996

thread_type:
official_review

<thread_object>
creator: reviewer_lkis

<content>
summary:
This paper proposes a framework for long-horizon imitation learning by combining the ‘automatic concept discovery’ module and the ‘concept-guided policy learning’ module. The former is trained without human annotations of the sub-sequences of robot trajectories. Instead, they assign the concept to the trajectories in a self-supervised manner.

strengths:
The paper proposes a method for hierarchical imitation learning by discovering the motion ‘concept’ without human annotation and motion policy conditioned on the discovered concept. The three tricks for discovering these concepts stably from trajectories seem to be reasonable.

weaknesses:
Some literature potentially relating to the method or problem setting of this paper is missing. For example, learning abstract action with clustering (BeT[1]) and with VQ-VAE[2]; please contrast this work with others listed above.
The proposal is tested with the Robosuite (MimicGen) benchmark, whose task is inherently highly compositional, so the concept discovery seems to be relatively easy. It is interesting to see the result with an “in-the-wild” dataset, such as dataset collected with a ‘learning from play’ manner [3].

[1] Behavior Transformers: Cloning k modes with one stone https://arxiv.org/abs/2206.11251
[2] Behavior Generation with Latent Actions https://arxiv.org/abs/2403.03181
[3] https://arxiv.org/abs/1903.01973

questions:
1)	Why is only proprioception used for the automatic concept? It seems natural to include observations in addition to proprioception to assign the abstract concept to the time step. How can we generalize it to observation? There may be a case where similar proprioception (e.g., robot pose) but a different observation (e.g., an obstacle in the environment).
2)	How do you decide the total number of concepts/codebooks of VQ-VAE (K)? How robust is the proposal in terms of the number of concepts?
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</thread>
<thread>
creation date:
1732211835093

thread_type:
official_comment

<thread_object>
creator: authors

<content>
comment:
We sincerely thank all reviewers for their insightful feedback and constructive comments on our manuscript. We are grateful to note that the reviewers appreciate the soundness of our concept-guided pipeline in addressing the robot manipulation problem (R1, R2, R3, R4). Furthermore, our work is acknowledged for providing a fresh perspective of concept discovery without the need of human annotations (R1, R3, R4). The reviewers have also echoed our solid experiment results across a range of comprehensive tasks, as well as the selection of appropriate baselines for comparison (R2, R3, R4). Additionally, we are grateful for the positive remarks on the clarity of our paper's presentation and writing quality (R3, R4).

In response to the reviewers' feedback, we have provided additional explanations and experiments to address their concerns. The manuscript has been revised accordingly, with all modifications highlighted in orange for ease of reference. A summary of the updates is as follows:

**Main Body**
- **Sec. 1:** Include a reference to Sec. A.1 in the Appendix, explaining the use of proprioceptive states (R#1). 
- **Sec. 2:** Add comparisons and analyses of works with similar objectives (R#1) and those related to our methodology (R#4).
- **Sec. 3.2:** Refine Eq. 10 and Eq. 11 for improved clarity and correctness (R#3).
- **Sec. 4.1:** Revise the implementation details of experiments (R#1, R#2, R#3).
- **Sec. 5:** Add Sec. D in the Appendix to detail future works, with references to it in this section (R#1, R#3, R#4).

**Appendix**
- **Sec. A.1:** Provide explanations and experiments justifying the use of proprioceptive states (R#1).
- **Sec. A.3:** Include additional references on the benefits of Hypernetworks (R#3).
- **Sec. B.2:** Expand on experimental details (R#1, R#2, R#3).
- **Sec. C.3:** Add visualizations to evaluate the consistency of discovered manipulation concepts (R#3) and additional real-world application results (R#4).

**Supplementary Material**
- Include some videos related to our rebuttal.

Once again, we sincerely thank all reviewers for their valuable feedback and thoughtful suggestions towards enhancing our manuscript. We have carefully addressed the queries and concerns raised by each reviewer. Should there be a need for further clarification to assist in advancing our score, please do not hesitate to reach out.

Thank you for your review!
</content>
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</thread>


Metareview:
metareview:
The paper presents a novel method for autonomously abstracting manipulation concepts from proprioceptive states. It introduces two main components: Automatic Concept Discovery (ACD), which identifies meaningful and consistent manipulation concepts by abstracting from low-level proprioceptive states, Concept-Aware Policy Learning (CAPL), which utilizes these manipulation concepts to guide task execution adaptively. The experiments, conducted in simulation, demonstrate significant improvements over state-of-the-art (SOTA) baselines.

Strengths:

- Innovative Methodology with Potential Impact: The proposed method is highly innovative, enabling the discovery of manipulation concepts without relying on human-annotated labels or predefined skills. This is a significant step forward in robotics research.
- Clarity and Technical Depth: The paper is well-written and well-structured, making the methodology and contributions easy to understand.
- Strong Experimental Validation: The comprehensive comparison with SOTA baselines, while limited to simulation, clearly demonstrates the effectiveness of the proposed approach.

Weaknesses:

- The experiments focus primarily on simulated tabletop manipulation tasks, with limited evaluation on more complex morphologies, diverse tasks, or real-world datasets. Expanding the experiments to include real-world settings and incorporating multimodal data would strengthen the paper’s claims. 

Despite these concerns, I believe the strengths and potential impact of the paper outweigh its weaknesses, making it a valuable contribution to the field. One minor point for improvement would be to discuss and compare the approach with "Discovering Robotic Interaction Modes with Discrete Representation Learning," published in CoRL just before the ICLR deadline. This work also seeks to learn manipulation concepts autonomously.

Overall, I wish the authors the best of luck and look forward to the final version of the paper.