ContactExplorer: Contact Coverage-Guided Exploration for General-Purpose Dexterous Manipulation

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

ContactExplorer (CCGE) introduces a novel, general exploration method for deep reinforcement learning in dexterous manipulation, addressing the common lack of general-purpose reward formulations. CCGE represents contact states as intersections between object surface points and predefined hand keypoints, encouraging the discovery of diverse and novel contact patterns. It maintains a contact counter, conditioned on discretized object states obtained via learned hash codes, to track finger-region interactions. This counter informs two complementary rewards: a count-based contact coverage reward for novel patterns and an energy-based reaching reward guiding agents toward under-explored regions. Evaluated on tasks like cluttered object singulation and constrained object retrieval, CCGE significantly improves training efficiency and success rates, achieving 88% in constrained retrieval and reducing sample complexity by 2-3x, with learned patterns transferring robustly to real-world systems.

Key takeaway

For robotics engineers and AI scientists developing DRL solutions for dexterous manipulation, CCGE offers a principled, task-agnostic exploration reward that significantly boosts learning efficiency and robustness. You should consider implementing its dual reward system—combining post-contact novelty with pre-contact guidance—and state-conditioned contact counters to overcome sparse reward challenges and reduce reliance on handcrafted task priors in complex manipulation scenarios.

Key insights

CCGE leverages state-conditioned contact coverage to enable efficient, general-purpose exploration in dexterous manipulation.

Principles

• Explicitly model hand-object contact for exploration.
• Condition exploration signals on object state clusters.
• Combine pre-contact guidance with post-contact novelty.

Method

CCGE discretizes object states using learned hash codes, tracks finger-region contact counts, and uses these for a post-contact novelty reward and a pre-contact energy-based reaching reward.

In practice

• Implement contact coverage for complex manipulation.
• Use learned state hashing to manage exploration.
• Integrate dual reward signals for DRL efficiency.

Topics

• Dexterous Manipulation
• Deep Reinforcement Learning
• Robotic Exploration
• Contact-Guided Control
• Reward Shaping
• Sim-to-Real Transfer

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.