DiPLO: Self-distilled Diffusion Planning for Loco-manipulation
Abstract
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.
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Research summary
DiPLO introduces a self-distilled diffusion planning framework that enables humanoid robots to perform complex loco-manipulation tasks from limited demonstrations by decoupling motion generation from execution and using a two-stage post-training process to remove dependence on kinematic references. The method demonstrates strong generalization to unseen goals and successful real-world deployment, highlighting the potential of hierarchical learning approaches for embodied AI.
Methodology
The paper proposes DiPLO, a hierarchical framework for humanoid loco-manipulation that decouples high-level motion planning (diffusion model) from low-level execution (RL tracking policy). The core methodological contribution is a two-stage post-training process: (1) Self-Distillation Diffusion Policy Optimization (SD:DPO), which distills a teacher model conditioned on kinematic references into a student model conditioned only on state history and goals, effectively removing the need for explicit task priors at deployment; and (2) Diffusion-Augmented Proximal Policy Optimization (DA:PPO), which fine-tunes the distilled planner using maximum-entropy RL to align generated plans with the frozen tracker's capabilities. The use of SO(3) diffusion for rotational features is a technically sound choice that respects the manifold geometry of rotations. The separation of planning and tracking allows the planner to learn diverse motion structures from limited data while the tracker learns robust feedback control via RL, addressing the mode-covering vs. mode-seeking trade-off inherent in generative models vs. RL.
Results & evaluation
The experimental evaluation is rigorous and comprehensive. The authors provide extensive ablations on pretraining data augmentation (SBTO vs. symmetry), the importance of task conditioning during pretraining, and the specific benefits of SO(3) diffusion. They compare motion-level planning against action-level diffusion baselines (PDP, BeyondMimic), demonstrating the superiority of the hierarchical approach for survival and task success. The generalization evaluation, where the robot achieves unseen goals up to 5 meters away, is a strong indicator of the method's robustness. The real-world deployment on a Unitree G1 humanoid, achieving 100% success in pick-carry-place tasks without real-world fine-tuning, provides strong evidence of practical viability.
Reproducibility
The paper provides detailed descriptions of the diffusion processes (VP diffusion for Euclidean features, IG_SO(3) for rotations), the DA-MDP formulation, and the training objectives. However, specific hyperparameters, network architectures (DiT details), and code availability are not explicitly provided in the text (no GitHub link found in the provided text). The reliance on specific datasets (OmniRetarget, OMOMO) and simulation environments (mjlab) may limit immediate reproducibility for those without access to these resources.
Limitations
The method relies on external motion capture for object pose estimation in the real-world setup, which is a significant practical limitation for general deployment. The planning frequency is limited to ~12.5 Hz due to diffusion sampling, which may be too slow for highly dynamic tasks requiring faster replanning. The evaluation is primarily focused on pick-carry-place and similar tasks; performance on more complex, multi-object, or highly dynamic interactions is not fully explored. The dependence on a frozen tracker means that if the tracker fails, the planner cannot adapt its strategy in real-time to compensate for execution errors beyond the replanning horizon.
Broader impact
This work contributes to the growing field of humanoid robotics by providing a scalable method for learning complex loco-manipulation skills from limited demonstrations. The insight that strong task conditioning is useful for pretraining but can be distilled away for deployment is a valuable principle for robot learning. The combination of diffusion models for planning and RL for tracking offers a robust paradigm for handling the non-stationarity and balance constraints inherent in humanoid control. DiPLO introduces a self-distilled diffusion planning framework that enables humanoid robots to perform complex loco-manipulation tasks from limited demonstrations by decoupling motion generation from execution and using a two-stage post-training process to remove dependence on kinematic references. The method demonstrates strong generalization to unseen goals and successful real-world deployment, highlighting the potential of hierarchical learning approaches for embodied AI.
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