Learning Object Manipulation from Scratch via Contrastive Interaction
Contrastive Reinforcement Learning (CRL) has seen recent success in a wide variety of goal-conditioned robotics tasks by learning structured representations of the dynamics. However, despite its success in locomotion and simpler control domains, CRL often struggles in interaction-rich manipulation. We argue that a key source of this difficulty is object-centric interaction, such as contact or grasping, that induces distinct changes in the underlying dynamic modes. In this work, we formulate manipulation dynamics as a piecewise-smooth Markov process and show that interaction-induced mode changes create piecewise nonlinear reachability structures that are difficult for standard CRL energy f...
| Authors | Tongle Shen, Caleb Chuck, Fan Feng et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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