Belief-Overlap Partial Inversion: Object-Centric Flow Replanning for Humanoid Loco-Manipulation

Learning a single perceptive policy for diverse humanoid loco-manipulation requires unifying high-level task decisions without losing the state-dependent physical responses that make each interaction succeed. This makes the representation between high-level planning and whole-body control critical: kinematic or contact targets provide compact abstractions but do not directly encode interaction-specific control responses, while directly predicting low-level actions requires the planner to model high-frequency closed-loop feedback. We propose FIBER, a framework that uses the executable latent space of a frozen behavioral foundation model (BFM) as a shared planning representation for heterogeneous interaction skills. Our latent policy projection (LPP) maps independently acquired task-specific policies into this representation by identifying latent commands whose induced actions match their local control responses, enabling lower-rate planning while retaining the BFM's high-frequency state feedback. After projection, we aggregate closed-loop latent rollouts from all skills and train a single perceptive flow planner entirely offline. We further introduce AH-BFM, an adaptive-horizon BFM trained with multi-gamma forward-backward (FB) learning to support both precise control and temporally extended whole-body behavior. We evaluate FIBER on box pickup, door opening, and object grasping in simulation and on a real Unitree G1 humanoid. Across 128 diverse variations per task in simulation, FIBER achieves 42.0% mean success, more than tripling projected latent replay (11.6%) and substantially outperforming kinematic planning (3.5%). Real-world experiments further demonstrate sim-to-real transfer, skill chaining, and conditional steering through the same unified planner.

Humanoid loco-manipulation is particularly challenging because it requires coordinating locomotion, balance, and object interaction, making it difficult to learn from limited demonstrations or from scratch through Reinforcement Learning (RL). We introduce DiPLO (self-distilled Diffusion Planning for Loco-manipulation), a framework that combines a self-distilled diffusion planner with a low-level whole-body tracking policy. Our planner is pretrained via supervised learning on motions derived from human-to-humanoid retargeting, while the tracker is trained via RL to execute them. DiPLO centers around two complementary post-training stages. First, we introduce Self-Distillation Diffusion Policy Optimisation (SD:DPO) to remove the planner's dependence on strong task conditioning, which we identify as a crucial ingredient for effective pretraining, as downstream fine-tuning does not recover comparable performance when it is omitted.. We then fine-tune this distilled model with Diffusion-Augmented Proximal Policy Optimization (DA:PPO), a maximum-entropy RL method for diffusion models, to improve task success and align generated plans with the frozen tracker's execution capabilities. Together, these stages enable motion generation through state-history and goal conditioning alone, enabling 99% task success in pick-carry-place tasks, compared to just 45% for the pretrained model. The resulting planner also generates motions for unseen object goals, with further evaluations on kicking and pushing/pulling tasks. Finally, we deploy our pipeline on the hardware, using our DA:PPO planner-tracker on a Unitree G1, achieving repeated successful executions without any real-world fine-tuning.

Video planning has emerged as a flexible framework for robot manipulation, in which a generative model predicts a video of task completion, and a downstream module translates the predicted frames into actions. However, existing methods typically ignore information from past interactions, limiting their ability to adapt to latent physical properties that can only be revealed through trial and error, such as whether a door should be pushed or pulled, or how friction affects object dynamics. When a plan fails, these methods usually replan from scratch without leveraging the information revealed by the failure. To address this limitation, we introduce RELIC, REplanning with Latent embedding refInement and Candidate rejection, a video planning framework that adapts to hidden physical properties from test-time failures. RELIC optimizes a latent embedding that captures the environment's hidden physical properties from interaction videos and introduces a rejection-based sampling mechanism that filters out hypotheses inconsistent with prior failures. Across eight tasks in two simulation suites, RELIC consistently reduces the number of replanning steps required for success, and linear probes show that its embedding captures the hidden parameters from the interaction itself rather than from scene appearance. Across four challenging real-world robotic manipulation tasks involving hidden interaction modes, e.g., friction, center of mass, and object mass, RELIC raises the one-shot replanning success rate of a video planning baseline from 30.0% to 63.8% after a single physical interaction.