Title: Chain-of-Action: Trajectory Autoregressive Modeling for Robotic Manipulation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Robotic Manipulation
Abstract: We present Chain-of-Action (CoA), a novel visuomotor policy paradigm built upon Trajectory Autoregressive Modeling. Unlike conventional approaches that predict next step action(s) forward, CoA generates an entire trajectory by explicit backward reasoning with task-specific goals through an action-level Chain-of-Thought (CoT) process. This process is unified within a single autoregressive structure: (1) the first token corresponds to a stable keyframe action that encodes the task-specific goals;  and (2) subsequent action tokens are generated autoregressively, conditioned on the initial keyframe and previously predicted actions. This backward action reasoning enforces a global-to-local structure, allowing each local action to be tightly constrained by the final goal. To further realize the action reasoning structure, CoA incorporates four complementary designs: continuous action token representation; dynamic stopping for variable-length trajectory generation; reverse temporal ensemble; and multi-token prediction to balance action chunk modeling  with global structure. As a result, CoA gives strong spatial generalization capabilities while preserving the flexibility and simplicity of a visuomotor policy. Empirically, we observe that CoA outperforms representative imitation learning algorithms such as ACT and Diffusion Policy across 60 RLBench tasks and 8 real-world tasks.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes to using backward reasoning to generate/predict the action chunks intead of all maintream method using next token prediction in a forward manner, claiming that this will enforge a global-to-local structure and better relaize the final goal.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The assumption that reasoning the model backward from the keyframe to the current state is intuitively rationale since in a foward manner, based on the characteristic of autoregressive model, the error will compound, leading to less and less accurate with increasing of the horizon.\n2. The shown results can support the claim that the proposed regularization of latent space, backward reasoning, reversal ensembling, multi-token prediction can all boost the performance to some extent.\n\nWeakness:\n1. The main simulation results shown in Table 1 seems not like a standard benchmark, as well as known, the standard 18 tasks shown in PerAct is well used for a standard reference for RLBench, why the author here using 10 self-selected tasks to eval the method, also the author mentioned it decrease the variation to 1, which is kind of suspicious compared with the standard benchmark in RLBench.\n\n2. The main results in Table 1 seems only compared with ACT and DP, which is kind of lack, how about it compared with the state-of-art method OpenVLA, which is a well known standard autoregressive method in forward reasonaing manner.\n\n3. The experiment and ablation parts use lots of space to explore the spatial distribution, which makes me confused, first the description for the experiment in Section 5.3 (the first two parts) is not clear what the author did, is that all about choosing and eval on different demonstrations? how you control the object distributions when generate the demos? what exact the interpolationa and extrapolation mean? Also, most impotant is I am confused and intuitively cannot relate the sptial properties of the policy and the proposed backward reasoning manner, seems the two does not have some casual relationship, i am not convinced by both the shown Figure 5,6 and the conceptual rationale.\n\nQUESTIONS:\nFirst, please refer to weakness to discuss the 3 main weakness, besides that, i still have some small quesitons as follows:\n1. I would like to understanding the author's opinion on: only predicting the target keyframe insteading of predicting action chunks from keyframe to current state in a reverse manner, that is, the whole paper rely on claim that using keyframe centered goal conditioned backward reasoning can better lead the model focus on its short-horizon local goal, intead of gradual lose attention and goal in a foward manner, so in that sense, the accuracy whould highly depends on the prediction of the keyframe given the current state, so how about only perdict the keyframe and continouly call go to pose until it gets there, what is the advantages of CoA compared to it?\n\n2. For the inference, how the exact reverse ensemble is implemented? is it aggregate n steps prediction and smooth it? how many steps is actually executed for each prediction call?\n\n3. what is the computation burden of the autoregressive model, since it seems the model should be able to modeling the longest length of a keyframe segment, wonder what is the exact context length of the model.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Chain-of-Action (CoA), a trajectory autoregressive model for robotic manipulation, which generates action sequences backward from goal keyframes to initial states, aimed at mitigating compounding errors and improving spatial generalization. The method is empirically demonstrated to outperform several baselines on the RLBench benchmark and real-world tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- The core idea of backward trajectory generation is interesting and potentially effective for addressing compounding errors in robotic manipulation tasks.\n- The authors demonstrate strong empirical results on extensive simulation benchmarks (60 RLBench tasks) and real-world tasks.\n- The approach is well-explained, with clearly presented visualizations.\n\nWeaknesses:\n- The integration-based stopping criterion appears effective for pick-and-place tasks but may be challenging for tasks involving frequent or prolonged contacts, such as locomotion or dexterous manipulation.\n- Previous works like ACT succeeded with only an L1 loss over actions. It is unclear why CoA specifically requires the temporal consistency loss to function effectively. Can the authors clarify why their method underperforms without this additional constraint, compared to existing methods?\n- Although the paper includes some ablation studies, it remains difficult to pinpoint which components of CoA are primarily responsible for its improved performance over baselines. Several elements (mtp, continuous actions, and temporal consistency loss) are known techniques. In my view, the truly novel aspects are the backward prediction and the use of variable-length action chunks. Table 2 partially addresses the backward prediction by comparing with a forward model, but it would be helpful to also include an ablation that combines forward prediction with fixed-length action chunks. This would better isolate the contribution of the reverse, variable-length modeling.\n- No statistical confidence / error bars. Results are means over 25 trials, but variance or CI is never reported.\n\nQUESTIONS:\n- The abstract claims an \"action-level Chain-of-Thought (CoT) process,\" yet the method primarily predicts flexible-length action chunks without explicit or latent reasoning steps. Could the authors clarify this discrepancy or better justify the labeling of their approach as CoT?\n- One of the unique properties of CoA is the flexible execution segment prediction. However, there is no analysis of that part. The average length of the execution segments is not clearly stated. Given that longer segments could introduce larger accumulative errors, it would be helpful to see an analysis of how segment length affects success rates across tasks.\n- What kinds of RLBench tasks still fail (see tails of Fig 4)? Are there qualitative patterns (e.g., high-precision alignments, deformable objects)?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 2, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents Chain-of-Action (CoA), a novel trajectory-level autoregressive modeling framework for robotic manipulation. Unlike conventional forward action generation, CoA introduces backward trajectory prediction, starting from a goal-anchored keyframe and generating actions in reverse. Empirical results show that CoA significantly outperforms prior methods such as ACT and Diffusion Policy on 60 RLBench tasks and 8 real-world kitchen tasks.\n\nSTRENGTHS AND WEAKNESSES:\n### Strength:\n1. Strong empirical results: CoA achieves strong performance across both simulation and real-world settings, outperforming several baselines.\n\n2. Comprehensive ablation and case studies: The reviewer appreciates the in-depth ablation study, which provides valuable insights to the contribution of each design choice. \n\n### Weakness:\n1. Motivation: It is unclear what the biggest motivation is for favoring the reverse autoregressive structure over the forward autoregressive structure. Is it because of the compounding error? If yes, it is worth investigating methods that tackle similar problems. \n\n2. Incremental novelty: This paper essentially proposes an improvement on top of the ACT paper, with goal-conditioning and reverse autoregressive. Goal-conditioning is a well-explored topic, and it is not surprising that it can improve the performance. The reverse autoregressive idea is interesting; however, I find the paper lacks theoretical grounding on the proposed idea, and insufficient discussion on the “chain-of-action”.\n\n3. Writing:  The writing can be improved. It would be helpful to explain the overall architecture and then dive deep into each component. Also, many terms are not properly defined. For example, what does the start-of-sequence (SOS) token and the final keyframe action refer to in line 166?\n\n4. Related work: It would be helpful to include more literature on goal-conditioned learning. \n\n5. Baseline: It would be interesting to see how the goal-conditioned forward autoregressive model and goal-conditioned diffusion model perform. These are important baselines to evaluate the contribution of this work.\n\nQUESTIONS:\n1. What is the reason that the proposed model failed on Press Switch, Reach Target, Sweep Dust and Open Box?\n2. Does the learned token $x_{SOS}$ refers to the latent variable learnt from the encoder? If the proposed network follows the design of ACT, then what is the distribution of this latent variable? \n3. Can the proposed method generalize to other model architectures other than CVAE?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents Chain-of-Action (CoA), a novel visuo-motor policy architecture for robotic manipulation, built upon trajectory autoregressive modeling with a reverse generation paradigm.\nCoA generates the actions by reasoning backward from a goal-specific keyframe action to the initial state.\nThis backward generation enforces a global-to-local structure, ensuring that every local action remains tightly aligned with the task objective and significantly mitigating compounding errors.\nThe authors introduce four key design elements to realize the CoA framework:\n- Continuous action token representation to avoid quantization errors.\n- Multi-token prediction to improve local action coherence.\n- Dynamic stopping for flexible trajectory lengths.\n- Reverse temporal ensemble to aggregate multiple backward rollouts.\nExperimental results on the RLBench benchmark (60 simulation tasks and 10 representative tasks) demonstrate that CoA achieves state-of-the-art performance, outperforming strong baselines such as ACT and Diffusion Policy (DP), with average improvements of 16% and 23% respectively. Notably, CoA shows superior spatial generalization, maintaining higher success rates even as the spatial variance of the environment increases, and proves more robust in out-of-distribution scenarios. Ablation studies confirm the importance of each design component. Real-world experiments on a Fetch robot further validate CoA’s effectiveness, achieving a 15% higher average success rate than ACT on eight kitchen manipulation tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- The proposed algorithm is both simple and intuitive. By leveraging a backward, goal-conditioned trajectory generation paradigm, the approach is conceptually easy to understand and implement, yet powerful in practice.\n- The method achieves strong empirical performance, consistently outperforming existing baselines across a wide range of simulated and real-world robotic manipulation tasks.\n- The paper provides extensive experimental analysis, including comprehensive comparisons to widely-used methods, thorough ablation studies, and in-depth exploration of spatial generalization and robustness, which together offer convincing evidence for the effectiveness of the proposed approach.\n\nWeaknesses\n- The method is limited to manipulation tasks where actions correspond directly to end-effector poses. Both the keyframe extraction heuristic and the distance-based stopping mechanism rely on this correspondence, making it difficult to generalize the approach to broader classes of robotic tasks (e.g., locomotion, multi-agent coordination, or tasks with more abstract action spaces).\n- While the algorithm’s simplicity and intuitive design are strengths, it raises the question of whether reverse-order planning is truly novel, or if similar ideas have been explored in previous work. A more thorough review of related literature on backward or reverse-order trajectory generation would strengthen the paper.\n\nQUESTIONS:\n- I am curious whether there are truly no existing works that adopt a similar reverse-order trajectory generation approach. If your response (or further discussion among reviewers) can clarify the novelty of CoA relative to prior literature, I would be inclined to rate the paper more favorably.\n- I would also like to better understand why the proposed method achieves better performance than Diffusion Policy. As I understand, Diffusion Policy also considers future states by denoising current actions, which seems to have a similar effect to the reverse-order generation in CoA. Is there a specific reason for CoA’s superior performance? For example, could it be due to the action sequence length? According to Figure 7, CoA generates sequences of about 80 steps, whereas Diffusion Policy typically generates only around 20 steps per trajectory. Is it possible that DP does not fully account for the keyframe action or long-horizon dependencies as effectively due to this shorter sequence length?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}