The field of robot learning and interaction is rapidly evolving, with a focus on developing more sophisticated and generalizable models for embodied agents. Researchers are exploring new approaches to learn from human videos, object-centric 3D motion fields, and language models to improve robot control policies and action understanding. Notably, innovative methods are being proposed to address challenges such as heterogeneous skeleton-based action representation learning, zero-shot temporal interaction localization, and handle-based mesh deformation guided by vision language models.

Some noteworthy papers in this area include:

• Towards a Generalizable Bimanual Foundation Policy via Flow-based Video Prediction, which proposes a novel bimanual foundation policy by fine-tuning text-to-video models to predict robot trajectories.
• InteractAnything: Zero-shot Human Object Interaction Synthesis via LLM Feedback and Object Affordance Parsing, which presents a framework for zero-shot 3D human object interaction generation without training on specific datasets.
• Rodrigues Network for Learning Robot Actions, which introduces a novel neural architecture specialized for processing actions by injecting kinematics-aware inductive bias into neural computation.