RE4: Transformation-aware Imitation of Object Interactions Using Manipulation Modes
Object interaction tasks have been a focus of advances in imitation learning. End-to-end methods, dominated by diffusion and flow-based variants have shown leaps in performance while sacrificing interpretability. Object-centric and pose-informed variants have had a role in learning from demonstration in manipulation tasks. In this paper, we revisit a few modern imitation learning benchmarks for object interactions, with the aim of composing a framework that repurposes principled theories of manipulation, preserving both performance and interpretability. For image observations, lightweight training is proposed for model-free pose estimation of the target object, using self-supervision over...
| Authors | Arsh Chawla, Rahul Shome |
|---|---|
| Primary category | cs.RO |
| Published | 23 June 2026 |
| Last updated at source | 23 June 2026 |
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