Research questionHow can robot-learning systems integrate perception, action, and reasoning for reliable long-horizon operation in unstructured environments?Robot-learning systems often develop perception, policy learning, and consequence prediction as separate components. Their fragmented representations make it difficult to transfer across settings, reason over extended interactions, and act reliably in unstructured environments. Latest papersRecent research connected to this question, newest first.MulDP: Multimodal Diffusion Policy for Autonomous Quadruped Parkour Navigation across Complex TerrainsThe source concerns a multimodal policy for quadrupeds that uses visual perception, proprioception, and goal information to generate navigation velocity commands. Evidence comes from simulation and real-world experiments and includes a multimodal quadruped parkour dataset; specific terrain coverage and deployment limits are not detailed.research paper · Sep 3, 2026Toward Unified Robot Learning: Bridging Representation, Vision-Language-Action, and World ModelsThe source is a survey covering representation learning, vision-language-action models, and world models for robotics. It discusses their interactions and limitations, including uncertainty quantification, out-of-distribution generalization, cross-embodiment transfer, long-context understanding, and long-horizon planning; it does not present a single integrated system or deployment result.research paper · Sep 3, 2026