Embrace Collisions: Humanoid Shadowing for Deployable Contact-Agnostics Motions (2502.01465v1)

Abstract: Previous humanoid robot research works treat the robot as a bipedal mobile manipulation platform, where only the feet and hands contact the environment. However, we humans use all body parts to interact with the world, e.g., we sit in chairs, get up from the ground, or roll on the floor. Contacting the environment using body parts other than feet and hands brings significant challenges in both model-predictive control and reinforcement learning-based methods. An unpredictable contact sequence makes it almost impossible for model-predictive control to plan ahead in real time. The success of the zero-shot sim-to-real reinforcement learning method for humanoids heavily depends on the acceleration of GPU-based rigid-body physical simulator and simplification of the collision detection. Lacking extreme torso movement of the humanoid research makes all other components non-trivial to design, such as termination conditions, motion commands and reward designs. To address these potential challenges, we propose a general humanoid motion framework that takes discrete motion commands and controls the robot's motor action in real time. Using a GPU-accelerated rigid-body simulator, we train a humanoid whole-body control policy that follows the high-level motion command in the real world in real time, even with stochastic contacts and extremely large robot base rotation and not-so-feasible motion command. More details at https://project-instinct.github.io

• The paper introduces a unified framework combining a transformer-based motion command interface with a deployable RL control policy for contact-agnostic humanoid motions.
• The methodology leverages GPU-accelerated simulations and zero-shot sim-to-real transfers to achieve complex maneuvers such as dynamic get-ups and ground spins.
• The results demonstrate enhanced robotic agility and robustness in contact-rich environments, offering notable applications in assisted living, search and rescue, and collaborative robotics.

Overview of "Embrace Collisions: Humanoid Shadowing for Deployable Contact-Agnostics Motions"

The paper proposes an advance in humanoid robotics by developing a unified motion framework that extends beyond the limitations of traditional bipedal locomotion and manipulation. Traditional humanoid systems predominantly focus on balancing and dexterous use of hands and feet, restricting full-body motion capabilities that humans naturally harness. This work addresses the gap by introducing a framework that enables humanoids to achieve contact-agnostic motions—actions that involve stochastic contacts with various parts of the robot's body besides the feet and hands, thus expanding the scope of interactions with the environment.

Research Contributions and Methodology

The core contribution of this research lies in two fundamental innovations: a versatile motion command interface and a deployable control policy trained through reinforcement learning (RL). The humanoid robotic systems are enabled to perform complex maneuvers such as getting up from a prone position or executing limb-intensive maneuvers through these contributions.

1. Motion Command Interface: The research highlights the inadequacy of current motion command interfaces which are predominantly designed for horizontal movement sequences. These are often insufficient when significant rotations or varied postures such as crawling or lying are involved. The researchers shifted to a contact-agnostic interface that embodies a more comprehensive kinematic motion sequence. The interface relies on a transformer-based motion command encoder to cope with varying numbers of motion inputs and includes key-frame-based commands tuned through reinforcement learning.
2. Deployable Control Policy: The authors introduce a training pipeline utilizing a GPU-accelerated simulation environment to devise whole-body control policies capable of executing the aforementioned motions. The concept of zero-shot sim-to-real transfer is central to this work, aiming for policy behaviors that transition seamlessly from simulation to real-world robots. They employ techniques like advantage mixing to reconcile sparse ROI rewards and dense regularization rewards, addressing challenges posed by the intrinsic complexity of humanoid motions under real-world constraints.

Results and Implications

The experimental results demonstrate that this framework significantly advances the control and capabilities of humanoid robots in real-time, contact-rich environments. The paper attests to an appreciable enhancement in the robot's ability to perform dynamic, whole-body motions such as getting up from lying down, complex ground spins, and balance-intensive tasks. A notable success rate is achieved in simulations, revealing the system's proficiency in handling severe contact scenarios and extolling the robustness of the proposed policy.

These findings have substantial implications for practical robotics applications. The extended agility and versatility of humanoid robots could revolutionize several domains, including assisted living, search and rescue operations, and collaborative human-robot environments. Theoretical implications also abound, suggesting a refined understanding of robot-environment physical interaction and augmented potential in humanoid morphology exploration.

Future Directions

The work opens avenues for continued exploration in several fronts. Primarily, integrating high-level artificial intelligence systems with the developed motion framework could foster more autonomous decision-making in complex interactions. Additionally, refining the framework to accommodate a broader range of environments could prove beneficial. Lastly, exploring further optimization of the encoder and reinforcement learning components to minimize computational demands and enhance real-time adaptability remains a key area for future research exploration.

In conclusion, this paper advances the field of robotics by addressing critical shortcomings in humanoid robot control and expanding their functional horizons. The methodologies and results presented lay a strong foundation for further innovations in the field, aspiring for more competent humanoid systems capable of diverse, interactive locomotion and manipulation tasks.