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Achieve zero-shot cross-embodiment visual tracking by dynamically adapting control policies to inferred embodiment constraints, eliminating the need for per-robot training.
Forget generic assistants – EgoSelf learns your habits from your first-person view data to predict your future interactions.
Video LLMs don't just get details wrong, they fundamentally distort motion and fabricate entire events, demanding a new approach to evaluation and mitigation.
Imagine populating any 3D environment with digital humans that spontaneously navigate and interact, driven only by visual input and goals.
Forget hand-crafted heuristics: this new dynamics-aware policy learns to exploit contact forces in cluttered environments, outperforming traditional methods by 25% in simulation and showing impressive sim-to-real transfer.
MLLMs that ace standard Referring Expression Comprehension benchmarks still stumble when faced with images designed to eliminate shortcuts, revealing a surprising lack of robust visual reasoning.
