ARMOR: Egocentric Perception for Humanoid Robot Collision Avoidance and Motion Planning (2412.00396v1)

Abstract: Humanoid robots have significant gaps in their sensing and perception, making it hard to perform motion planning in dense environments. To address this, we introduce ARMOR, a novel egocentric perception system that integrates both hardware and software, specifically incorporating wearable-like depth sensors for humanoid robots. Our distributed perception approach enhances the robot's spatial awareness, and facilitates more agile motion planning. We also train a transformer-based imitation learning (IL) policy in simulation to perform dynamic collision avoidance, by leveraging around 86 hours worth of human realistic motions from the AMASS dataset. We show that our ARMOR perception is superior against a setup with multiple dense head-mounted, and externally mounted depth cameras, with a 63.7% reduction in collisions, and 78.7% improvement on success rate. We also compare our IL policy against a sampling-based motion planning expert cuRobo, showing 31.6% less collisions, 16.9% higher success rate, and 26x reduction in computational latency. Lastly, we deploy our ARMOR perception on our real-world GR1 humanoid from Fourier Intelligence. We are going to update the link to the source code, HW description, and 3D CAD files in the arXiv version of this text.

• The paper introduces ARMOR, an egocentric perception system that uses distributed low-cost ToF sensors to enhance spatial awareness and broaden the field of view.
• It implements ARMOR-Policy, a transformer-based imitation learning strategy trained on 86 hours of human movement data to navigate dense environments effectively.
• Testing reveals a 63.7% reduction in collisions and a 26-fold decrease in computational latency, significantly enhancing task success and operational efficiency.

Analysis of ARMOR: Egocentric Perception for Humanoid Robot Collision Avoidance and Motion Planning

The paper "ARMOR: Egocentric Perception for Humanoid Robot Collision Avoidance and Motion Planning" explores the challenges and solutions in enhancing the perception and motion planning of humanoid robots in complex environments. The authors, a collaborative team from Carnegie Mellon University and Apple, propose ARMOR, a novel approach that integrates low-cost, distributed Time-of-Flight (ToF) sensors into humanoid robots to increase their spatial awareness and facilitate more efficient and collision-free motion planning.

Key Contributions

1. Egocentric Perception System: The authors present ARMOR, an egocentric perception hardware and software system that equips humanoid robots with wearable-like depth sensors. This setup dramatically enhances the robot's ability to perceive its environment without relying on stationary external cameras. By distributing multiple ToF sensors across the mechanical "skin" of the robot, ARMOR minimizes common occlusion issues and increases the overall field of view, addressing a crucial shortcoming in the current humanoid robotics systems.
2. Integration of ARMOR-Policy: To extend the capabilities of their perception system, the researchers developed ARMOR-Policy, a transformer-based imitation learning policy trained in simulation. This policy allows the humanoid robot to navigate densely packed environments by effectively leveraging information from around 86 hours of human movement data, specifically curated from the AMASS dataset, demonstrating significant advances in collision avoidance.
3. Performance Metrics: The real-world implementation and testing indicate that ARMOR offers significant improvements in collision reduction and success rates compared to traditional exocentric setups. Specifically, ARMOR achieved a 63.7% reduction in collisions and increased task success by 78.7% in comparison to a head-mounted camera system.
4. Computational Efficiency: One notable outcome is ARMOR's efficiency in computation. The proposed approach offers a 26-fold reduction in computational latency over the sampling-based motion planning baseline (cuRobo), evidencing that ARMOR can maintain real-time effectiveness in dynamic environments without compromising safety or success rates.

Theoretical and Practical Implications

The ARMOR system advances both practical and theoretical aspects of humanoid robotics. Practically, it offers scalable solutions for dynamic environments, showing potential applications in personal caregiving, industrial automation, and collaborative robots. The system's reliance on low-cost, scalable sensor configurations makes it an attractive option for widespread deployment and integration.

From a theoretical standpoint, the research explores the applicability of transformer-based models in motion prediction within robotics, furthering our understanding of imitation learning in dynamic robotic systems. This effort underscores the importance of strategic perception in developing intelligent and autonomous robots capable of seamless interaction with humans and their environments.

Future Directions

This research opens several pathways for further exploration. Enhancements in sensor technology, particularly those offering higher resolutions or more robust environmental penetration, might further improve ARMOR's perception capabilities. Moreover, expanding ARMOR-Policy beyond collision avoidance to include more complex skill sets such as manipulation tasks offers promising potential. The integration of ARMOR-style perception with advanced human-robot interaction models could significantly impact fields reliant on human-assistive robotics.

In summary, the ARMOR system represents a significant advancement in humanoid robot perception and motion planning. Its demonstrated efficiency and effectiveness underscore its potential to reshape our approaches to robot design and implementation in real-world environments.