Teaching Robots to Walk, Adapt, and Recover

Author: Denis Avetisyan

A new approach combines learned behaviors with reinforcement learning to create more resilient and versatile humanoid robots.

A two-stage adaptive humanoid control framework first establishes foundational behaviors through the distillation of independently trained policies on flat terrain, then refines this distilled policy with reinforced fine-tuning—a process employing gradient surgery to resolve conflicting updates and behavior-specific critics to enhance value estimation.

Researchers present a two-stage framework, Adaptive Humanoid Control, leveraging behavior distillation and reinforced fine-tuning to improve locomotion and recovery skills across varied terrains.

Despite advances in humanoid robotics, achieving truly adaptable locomotion remains challenging due to the limitations of skill-specific controllers in unstructured environments. This paper, ‘Towards Adaptive Humanoid Control via Multi-Behavior Distillation and Reinforced Fine-Tuning’, introduces a novel framework that learns a unified controller capable of seamlessly switching between diverse skills—such as walking, running, and recovery—across varied terrains. By combining multi-behavior distillation with reinforced fine-tuning, the proposed Adaptive Humanoid Control (AHC) demonstrates robust performance in both simulation and real-world experiments on a Unitree G1 robot. Could this approach pave the way for more versatile and resilient humanoid robots capable of navigating the complexities of real-world scenarios?

## Robust Locomotion: Embracing Imperfection

Developing robust locomotion in humanoid robots presents a significant challenge due to the unpredictability of real-world environments. Unlike simulations, uneven terrain, obstacles, and disturbances frequently disrupt balance. Traditional control methods falter under these conditions, necessitating adaptive strategies.

Approaches reliant on precise mapping or calibrated motor control prove brittle, while reactive balance control often results in inefficient movements. Achieving adaptable movement requires learning and control that extends beyond pre-programming.

The robot successfully demonstrates recovery and locomotion in real-world scenarios, including standing from prone and lying positions on sloped terrain, as well as recovering from external disturbances during walking.

Recent investigations explore reinforcement learning and model predictive control, enabling robots to learn robust policies directly from experience. This paradigm shift fosters autonomous and resilient locomotion.

The pursuit isn’t to mimic life, but to reveal the elegance within the physics itself.

## Learning to Walk: An Adaptive Framework

An Adaptive Humanoid Control framework leverages Reinforcement Learning to acquire robust locomotion skills, dynamically adjusting gait and balance in response to environmental changes.

The system utilizes Proximal Policy Optimization (PPO), a policy gradient method, for efficient control policy updates. PPO’s clipped surrogate objective promotes stable learning and sample efficiency. This enables the robot to learn complex behaviors with reasonable data and computational resources.

A two-stage framework, utilizing behavior distillation and reinforced fine-tuning, overcomes the challenges of directly learning multiple skills via multi-task reinforcement learning, enabling the acquisition of diverse humanoid robot skills and generalization to complex terrains.

The adaptive framework maintains stability and resilience on uneven terrain or under external forces, refining its control policy through continuous learning.

## Bridging the Gap: Simulation to Reality

Domain Randomization enhances learning agent robustness by training across diverse simulated environments, varying parameters like terrain, lighting, and object placement. This exposure promotes generalization and reduces the sim-to-real gap.

An Adversarial Motion Prior refines the learning process by guiding the agent toward natural movements, learned from real-world motion capture data. This prior regularizes the learning process, encouraging efficient and believable locomotion.

During second-stage fine-tuning, the use of PCGrad and behavior-specific critics results in higher and more balanced returns across tasks, indicating improved performance.

Managing conflicting gradients in multi-task learning requires careful optimization. Gradient Surgery and Behavior-Specific Critics isolate and optimize performance on individual skills, increasing cosine similarity between task gradients and improving generalization.

## Validation and the Path Forward

The Adaptive Humanoid Control framework was successfully implemented and evaluated on the Unitree G1 robot, achieving a higher success rate compared to existing control methods like HOMIE and HoST. Performance gains stem from the system’s ability to dynamically adjust to environmental challenges.

Knowledge transfer is facilitated through Policy Distillation, streamlining a complex policy into a simpler representation for efficient deployment on onboard hardware, maintaining substantial performance while reducing computational demands.

Policies incorporating behavior-specific critics demonstrate more stable value learning during second-stage fine-tuning, in contrast to those utilizing shared critics.

Robust recovery from fallen postures is achieved, in part, through behavior-specific critics, which minimize value loss during training. These critics allow for focused learning and reliable upright recovery. Abstractions age, principles don’t.

The pursuit of adaptive humanoid control, as detailed in this work, necessitates a ruthless simplification of complex systems. The framework champions distilling multiple behaviors into a unified, robust policy, echoing a sentiment held by the mathematician Carl Friedrich Gauss: “If I have seen as far as most men, it is because I have stood on the shoulders of giants.” This principle applies directly to the multi-behavior distillation stage; the robot doesn’t reinvent locomotion for each terrain, but builds upon pre-existing skills – the ‘shoulders’ – to achieve adaptation. The research elegantly demonstrates that true advancement isn’t about adding layers of complexity, but about identifying and leveraging fundamental principles, streamlining the system to its essential components for resilient performance across diverse terrains. It’s a study in elegant efficiency.

What’s Next?

The presented framework, while demonstrating a functional convergence of behavior distillation and reinforcement, merely sketches the boundary of a much larger, and likely more chaotic, problem space. The assumption of pre-defined, discrete behaviors, even when ‘distilled’ from complex demonstrations, feels increasingly… generous. Terrain is not a taxonomy. Recovery is not a checklist. The true challenge lies not in teaching a robot to react, but in minimizing the need for reaction in the first place.

Future iterations should, therefore, shift focus from behavior replication to proactive simplification. Can the system learn to actively sculpt its environment – or its own morphology – to reduce the demands of locomotion? The current reliance on gradient surgery, though effective, hints at a deeper inefficiency. It is a bandage, not a cure. A more elegant solution would anticipate instability, and preemptively adjust – not correct – for it.

Ultimately, the field must confront the implicit desire for complete control. Perhaps the most fruitful avenue of research lies in embracing a degree of ‘controlled falling’ – allowing the robot to exploit dynamic instability, rather than perpetually resisting it. Such an approach demands a re-evaluation of success metrics. Robustness isn’t about surviving everything; it’s about minimizing the consequences of what will inevitably happen.

Original article: https://arxiv.org/pdf/2511.06371.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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