GeCCo - a Generalist Contact-Conditioned Policy for Loco-Manipulation Skills on Legged Robots

2025-09-22

AI Summary

Existing quadrupedal locomotion methods predominantly rely on end-to-end deep reinforcement learning (DRL), requiring task-specific reward engineering for each new behavior—limiting scalability and reusability. This work introduces GeCCo (Generalized Contact-Conditioned policy), a universal low-level controller trained via DRL to robustly track arbitrary foot contact locations conditioned on real-time contact states. Unlike prior approaches, GeCCo enables plug-and-play deployment of a single low-level policy across diverse high-level tasks—including walking, obstacle negotiation, and button pressing—without retraining. By decoupling high-level task planning from low-level contact control, the framework significantly improves modularity and deployment efficiency. Experiments demonstrate strong generalization and robustness across complex terrains and multi-task scenarios, validating GeCCo’s effectiveness in bridging perception, planning, and control. This work establishes a scalable, modular paradigm for embodied locomotion control, advancing the design of adaptive and reusable legged agents.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Most modern approaches to quadruped locomotion focus on using Deep Reinforcement Learning (DRL) to learn policies from scratch, in an end-to-end manner. Such methods often fail to scale, as every new problem or application requires time-consuming and iterative reward definition and tuning. We present Generalist Contact-Conditioned Policy (GeCCo) -- a low-level policy trained with Deep Reinforcement Learning that is capable of tracking arbitrary contact points on a quadruped robot. The strength of our approach is that it provides a general and modular low-level controller that can be reused for a wider range of high-level tasks, without the need to re-train new controllers from scratch. We demonstrate the scalability and robustness of our method by evaluating on a wide range of locomotion and manipulation tasks in a common framework and under a single generalist policy. These include a variety of gaits, traversing complex terrains (eg. stairs and slopes) as well as previously unseen stepping-stones and narrow beams, and interacting with objects (eg. pushing buttons, tracking trajectories). Our framework acquires new behaviors more efficiently, simply by combining a task-specific high-level contact planner and the pre-trained generalist policy. A supplementary video can be found at https://youtu.be/o8Dd44MkG2E.

Problem

• Training specialized controllers for each robot task is inefficient
• Existing methods require time-consuming reward tuning for new applications
• Lacking a general low-level policy for diverse loco-manipulation skills

Innovation

• Contact-conditioned policy trained with DRL
• Modular low-level controller for diverse tasks
• Combines high-level planner with pre-trained policy