Collision-Free Humanoid Traversal in Cluttered Indoor Scenes
HumanoidPF encodes humanoid-obstacle relationships, enabling collision-free navigation in cluttered indoor scenes with success rate over 93%.
Key Findings
Methodology
This paper introduces HumanoidPotentialField (HumanoidPF), an improved artificial potential field model that encodes the relationships between humanoid robots and obstacles as continuous, differentiable gradient fields. It samples the potential field at key body parts to serve as observation inputs, guiding reinforcement learning (RL) policies. A priority-weighting scheme resolves multi-modal conflicts by emphasizing critical body parts, while von Mises–Fisher distribution models motion direction preferences, promoting coordinated whole-body control. The training leverages hybrid scene generation—combining realistic 3D indoor scene crops with procedurally synthesized obstacles—to enhance robustness and generalization. Multiple specialist policies are trained in parallel and distilled into a generalist policy via DAgger, enabling scalable learning across diverse environments. The approach is deployed in a teleoperation system called Click-and-Traverse (CAT), allowing users to command the humanoid with a single click in real-world cluttered scenes.
Key Results
- In simulation, the method achieves a success rate of 93.9% across 8 obstacle configurations, with a distance error of only 0.08 meters, outperforming baselines like ASTraversal and Humanoid Parkour by 20-30%.
- In real indoor environments, the humanoid successfully navigates complex clutter with success rates exceeding 95%, demonstrating effective sim-to-real transfer.
- Ablation studies show that removing HumanoidPF observation or reward reduces success by over 50%, confirming its critical role.
Significance
This work advances humanoid navigation in cluttered indoor environments by systematically encoding the spatial relationships via potential fields, significantly improving RL training efficiency and generalization. Its successful transfer to real robots marks a step toward practical autonomous indoor service robots, with broad applications in household, rescue, and inspection scenarios. The approach addresses longstanding challenges of collision avoidance in high-dimensional, complex geometries, paving the way for safer, more reliable humanoid robots.
Technical Contribution
Key innovations include: (1) the development of HumanoidPF, a multi-point, continuous potential field encoding full-body obstacle relationships; (2) a priority-weighting scheme to resolve multi-modal conflicts; (3) the use of von Mises–Fisher distribution for action direction modeling; (4) hybrid scene generation combining real scene crops with procedural obstacles; (5) multi-policy training and distillation for scalable, robust generalization. These contributions collectively enable efficient, transferable, and scalable humanoid navigation.
Novelty
This is the first systematic application of potential fields tailored for humanoid whole-body navigation in cluttered indoor scenes. It innovatively combines multi-point sampling, priority weighting, and probabilistic directional modeling to solve multi-modal conflicts, surpassing prior works limited to terrain or partial obstacle layouts. The hybrid scene generation further enhances robustness, making this approach uniquely capable of handling real-world complexity.
Limitations
- The method relies on static obstacle assumptions; dynamic obstacles or highly unpredictable environments may challenge performance. Future work should incorporate dynamic perception and adaptive planning.
- Training involves high computational costs due to large-scale simulation and scene diversity, which could hinder real-time deployment without optimization.
- In extremely narrow or geometrically complex spaces, local minima or conflicting potentials may still occur, requiring further refinement of the potential field formulation.
Future Work
Future directions include integrating visual perception for dynamic obstacle handling, reducing computational complexity for real-time deployment, and extending to multi-robot coordination. Additionally, exploring end-to-end learning frameworks and adaptive potential field designs could further improve robustness and efficiency in complex, changing environments.
AI Executive Summary
The quest for autonomous humanoid robots capable of navigating cluttered indoor environments has long challenged researchers. Traditional path planning methods often struggle with complex obstacle geometries and dynamic changes, limiting real-world applicability. This paper introduces HumanoidPF, a novel potential field-based representation that encodes the full-body relationships between humanoid robots and obstacles as continuous gradient fields. By sampling these fields at key body parts, the approach provides dense, anticipatory guidance for reinforcement learning policies, significantly improving collision avoidance and navigation robustness.
The core innovation lies in the reformulation of classical artificial potential fields tailored specifically for humanoids, incorporating multi-point sampling, priority weighting, and probabilistic directional modeling. These mechanisms resolve multi-modal conflicts and promote coordinated whole-body control. To ensure broad applicability, the authors develop a hybrid scene generation pipeline that combines realistic indoor scene crops with procedurally synthesized obstacles, creating diverse, challenging training environments. This enables the learned policies to generalize effectively across unseen, complex scenarios.
Extensive experiments in simulation demonstrate success rates exceeding 93%, with a mere 0.08-meter average distance error, outperforming existing methods by significant margins. The policies are successfully transferred to real-world humanoid robots, validated through a teleoperation system called Click-and-Traverse, where users can command robots with a single click to navigate cluttered indoor spaces.
This work marks a substantial step forward in humanoid autonomous navigation, addressing longstanding issues of collision avoidance in high-dimensional, intricate environments. Its practical implications span household assistance, rescue missions, and industrial inspection. Future research will focus on dynamic obstacle adaptation, computational efficiency, and multi-robot coordination, aiming to realize fully autonomous, robust humanoid navigation in real-world settings.
Deep Dive
Abstract
We study the problem of collision-free humanoid traversal in cluttered indoor scenes, such as hurdling over objects scattered on the floor, crouching under low-hanging obstacles, or squeezing through narrow passages. To achieve this goal, the humanoid needs to map its perception of surrounding obstacles with diverse spatial layouts and geometries to the corresponding traversal skills. However, the lack of an effective representation that captures humanoid-obstacle relationships during collision avoidance makes directly learning such mappings difficult. We therefore propose Humanoid Potential Field (HumanoidPF), which encodes these relationships as collision-free motion directions, significantly facilitating RL-based traversal skill learning. We also find that HumanoidPF exhibits a surprisingly negligible sim-to-real gap as a perceptual representation. To further enable generalizable traversal skills through diverse and challenging cluttered indoor scenes, we further propose a hybrid scene generation method, incorporating crops of realistic 3D indoor scenes and procedurally synthesized obstacles. We successfully transfer our policy to the real world and develop a teleoperation system where users could command the humanoid to traverse in cluttered indoor scenes with just a single click. Extensive experiments are conducted in both simulation and the real world to validate the effectiveness of our method. Demos and code can be found in our website: https://axian12138.github.io/CAT/.