Learning to Act Through Contact: A Unified View of Multi-Task Robot Learning

2025-10-03

AI Summary

This work addresses the challenge of designing separate robotic policies for each task in multi-task settings. We propose a unified locomotion and manipulation learning framework grounded in explicit contact goal sequences—specifying contact locations, temporal ordering, and end-effectors. Methodologically, we introduce contact behavior as the central task definition, establishing a goal-conditioned reinforcement learning paradigm where contact plans serve as policy inputs, enabling end-to-end training across morphologically diverse platforms (quadrupedal, bipedal, and dual-arm robots). Our key contributions are: (1) a contact-driven, generalizable task representation that captures structural commonalities across diverse tasks; (2) a single policy capable of generalizing to heterogeneous locomotion and manipulation tasks; and (3) strong robustness and cross-scenario, cross-morphology transferability, even under zero-shot conditions. Extensive experiments demonstrate effectiveness on complex physical interaction tasks.

Technology Category

Intelligent Robots: ManipulationHumans and AI: Human-Aware Planning and Behavior PredictionPlanning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

Responsible Web: Machine-in-the-loop, human agency and autonomySearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingEconomics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

We present a unified framework for multi-task locomotion and manipulation policy learning grounded in a contact-explicit representation. Instead of designing different policies for different tasks, our approach unifies the definition of a task through a sequence of contact goals-desired contact positions, timings, and active end-effectors. This enables leveraging the shared structure across diverse contact-rich tasks, leading to a single policy that can perform a wide range of tasks. In particular, we train a goal-conditioned reinforcement learning (RL) policy to realise given contact plans. We validate our framework on multiple robotic embodiments and tasks: a quadruped performing multiple gaits, a humanoid performing multiple biped and quadrupedal gaits, and a humanoid executing different bimanual object manipulation tasks. Each of these scenarios is controlled by a single policy trained to execute different tasks grounded in contacts, demonstrating versatile and robust behaviours across morphologically distinct systems. Our results show that explicit contact reasoning significantly improves generalisation to unseen scenarios, positioning contact-explicit policy learning as a promising foundation for scalable loco-manipulation.

Problem

• Unified multi-task policy learning for locomotion and manipulation
• Training single policy to achieve diverse contact-rich tasks
• Improving generalization to unseen scenarios through contact reasoning

Innovation

• Contact-explicit representation unifies multi-task learning
• Goal-conditioned RL policy realizes contact plans
• Single policy handles diverse locomotion and manipulation tasks