This thesis investigates what the high level goal predictor should predict, how the low-level policy should consume it, what capabilities it must encode, and how a user might steer it all remain open through three studies that share one object of study: the high-level goal predictor.
A mechanism-by-mechanism transition to the recurrent behavior-cloning baseline shows that replacing the predictive pathway with direct observation input produces the largest single performance drop, accounting for approximately $70\% of the endpoint gap.
Dynin-Robotics achieves competitive performance on LIBERO and zero-shot LIBERO-Plus, together with a 78.4% average success rate across four manipulation conditions on a Franka Research 3 robot.
Hoe-hwan Lee, Jaeik Kim, Jusang Oh et al.· 0 citations
Hierarchical Robotic Control (HiRoC) is proposed, a hierarchical post-training framework that decouples high-level task planning from low-level action execution and aligns the executor with planner-generated subgoals before reinforcement learning, mitigating the distribution misalignment between planning and execution.
These results indicate that reliable long-horizon offline control emerges when expressivity is confined to execution and decision-making operates over support-aligned latent abstractions.
Talha Zaidi, Arslan Munir, Sardar Ali Abbas· Proceedings of the Thirty-Fi...· 0 citations
WorldToken, a time-first policy instantiation that fuses multiview images, proprioception, and task conditioning within each policy timestep into one world token is introduced and its data-scaling and temporal-context behavior under the tested recipes are characterized.
Chunkai Yang, An-Dong Yang, Di Huang et al.· 0 citations
This recipe for loco-manipulation generalist policies is replicated and extended to multi-object training, a first step toward loco-manipulation generalist policies at this data scale.
Omar Rayyan, Zhi Li, Max Argus et al.· 1 citation
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