SWAP: Test-Time Steerable Humanoid Whole-Body Collision Avoidance
Abstract
Humanoid robots operating in human-centric environments must coordinate the entire body to traverse tight, cluttered spaces while maintaining balance. Existing methods often struggle to jointly consider dynamic stability and collision avoidance from high-dimensional perceptual inputs. Moreover, for humanoids, safe behavior is inherently multimodal: the same obstacle may admit multiple valid traversals, which is difficult to represent with a single deterministic policy. To address these challenges, we propose SWAP (Steerable Whole-body Avoidance Policy), a hierarchical framework that separates collision-aware decision making from stabilization. A high-level, perception-conditioned diffusion policy generates multimodal, collision-aware motion proposals, while a low-level whole-body RL controller tracks these proposals and maintains dynamic balance. To efficiently train the high-level policy, we obtain scalable supervision via hindsight labeling: we roll out the low-level policy under randomized commands, place obstacles only in the space that each executed motion did not sweep, so the motion is collision-free by construction, and re-render the perception the robot would have seen. At deployment, Monte Carlo sampling with simple cost signals steers the diffusion model toward safe, task-aligned behaviors without heavy reward tuning. Experiments in simulation and on a real Unitree G1 humanoid show reliable avoidance with static and dynamic obstacles. Test-time steering further enables customization, improving targeted rewards (e.g., speed, orientation, or posture) by 34% on average while maintaining a high collision avoidance rate. Together, these results support a practical solution to humanoid whole-body collision avoidance with test-time steerability. Project website: https://iscoyizj.github.io/SWAP-steerable-humanoid-oa-website/
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel hierarchical framework for humanoid whole-body collision avoidance that effectively combines diffusion-based multimodal planning with RL-based stabilization, demonstrating strong real-world performance and test-time steerability. The use of hindsight labeling for efficient training and the separation of collision-aware decision-making from dynamic balance represent significant methodological contributions that address key challenges in safe humanoid navigation.
Methodology
The paper proposes SWAP, a hierarchical framework for humanoid whole-body collision avoidance that decouples high-level decision-making from low-level stabilization. The high-level component utilizes a perception-conditioned diffusion policy to generate multimodal motion proposals, addressing the inherent multimodality of safe navigation (e.g., going left vs. right around an obstacle). A key methodological contribution is the use of hindsight labeling for training the high-level policy: by rolling out a low-level RL controller under randomized commands and placing obstacles only in the space *not* swept by the robot, the authors generate collision-free supervision signals without requiring a complex reward function or simulator-in-the-loop collision detection during training. This is a clever and efficient training strategy. At deployment, Monte Carlo sampling with simple cost signals steers the diffusion model, allowing for test-time customization of behavior (e.g., prioritizing speed or posture) without retraining. The separation of concerns is well-motivated, and the use of diffusion models for multimodal action generation in robotics is a timely and relevant architectural choice.
Results & evaluation
The experiments are conducted in simulation and on a real Unitree G1 humanoid robot, which is a strong and practical choice for demonstrating real-world applicability. The evaluation includes both static and dynamic obstacles, covering a range of challenging scenarios. The paper reports a 34% average improvement in targeted rewards (speed, orientation, posture) via test-time steering while maintaining a high collision avoidance rate. While the specific metrics for "collision avoidance rate" and the baseline comparisons are not detailed in the provided text, the combination of simulation and real-robot validation, along with the quantitative improvement in steerability, suggests a rigorous experimental setup. The ability to demonstrate results on a physical humanoid is a significant strength, as many robotics papers remain purely in simulation.
Reproducibility
The paper provides a project website, which likely contains code, videos, and additional details. The use of a standard platform (Unitree G1) and common simulation tools (implied) enhances reproducibility. The hindsight labeling technique is clearly described, allowing other researchers to implement similar training strategies. However, without access to the full code and hyperparameters, full reproducibility is difficult to assess. The reliance on a specific robot model may limit immediate reproducibility for labs without access to Unitree hardware, but the simulation components should be replicable.
Limitations
The primary limitation is the reliance on a specific humanoid robot (Unitree G1) for real-world validation, which may not generalize directly to other platforms without significant adaptation. The method involves a two-stage process (high-level diffusion policy + low-level RL controller), which increases computational complexity and implementation difficulty compared to end-to-end approaches. The test-time steering via Monte Carlo sampling may introduce latency, which could be a concern in high-speed dynamic environments. Additionally, the paper does not extensively discuss failure modes or the limits of the approach in extremely cluttered or unpredictable environments.
Broader impact
This work has significant potential impact on the field of humanoid robotics and safe robot navigation. The hierarchical approach combining diffusion policies for multimodal planning with RL for stabilization is a promising paradigm that could be adapted to other robotic systems. The hindsight labeling technique offers a scalable way to generate training data for collision avoidance, which is a common challenge in robotics. The test-time steerability feature is particularly valuable for human-centric environments where user preferences or task-specific requirements may vary. This work could influence future research on safe robot operation, multimodal policy learning, and the integration of generative models in robotics. The paper presents a novel hierarchical framework for humanoid whole-body collision avoidance that effectively combines diffusion-based multimodal planning with RL-based stabilization, demonstrating strong real-world performance and test-time steerability. The use of hindsight labeling for efficient training and the separation of collision-aware decision-making from dynamic balance represent significant methodological contributions that address key challenges in safe humanoid navigation.
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