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It is shown that JEPA features outperform video VAE latents, most clearly out of distribution, and that training-time future prediction choices significantly impact the policy even when future prediction is skipped at inference, suggesting that robot foundation models may benefit from architectures designed for control...
Hierarchical planning frameworks combine skills from multiple robot control policies for long-horizon task execution, where determining when to terminate the current skill and advance to the next subtask is essential. Existing approaches often rely on pre-designed completion signal checkers that are hard to obtain in r...
A novel vision-language model (VLM)-based navigation framework that addresses challenges by adaptively retrieving task-relevant cues from a global memory module and integrating them with the agent's egocentric observations and enhances spatial reasoning and decision-making in long-horizon tasks.
Faster-WAM, an instantiation of DoT for WAMs, which docks a single-layer action head onto a 30-layer video backbone, achieves competitive performance on LIBERO and RoboTwin 2.0 while demonstrating strong out-of-distribution generalization on LIBERO-Plus.
RoboHarness is proposed, a unified framework that encapsulates independently developed robot control systems as reusable agentic skills as well as a general framework compatible with a broader range of robot policies, such as navigation policies, model predictive controllers, and world-action models.
Future-State-Conditioned VLN (FSC-VLN), a deployable model that augments a causal policy with a future-query token and uses training-only future-state supervision to distill information from future observations into the policy state, is proposed.
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