By constraining strategic exploration through a pretrained motion decoder, RoboStriker substantially reduces the catastrophic balance failures observed in raw action-space methods and achieves superior tactical performance in both competitive win rates and striking efficiency.
Abstract
Achieving human-level competitive intelligence and physical agility in humanoid robots remains a profound challenge, particularly in contact-rich and highly dynamic tasks such as boxing. While Multi-Agent Reinforcement Learning offers a principled framework for strategic interaction, its direct application to unstructured raw motor spaces inevitably leads to joint-level physical collapse, preventing the emergence of any viable combat tactics. To resolve this fundamental conflict between strategic exploration and physical feasibility, we formulate the humanoid combat task as a novel two-player latent-space zero-sum Markov game. Under standard regularity and approximate best-response assumptions, we show that the latent formulation induces an equivalent game over the decoder-reachable action manifold, providing an approximate-Nash interpretation of the resulting self-play dynamics. To instantiate this theoretical formulation, we propose RoboStriker, a hierarchical framework that decouples high-level reasoning from low-level execution. It first distills the tracking expertise of predefined boxing motions into a topologically bounded latent manifold. This structured latent foundation subsequently drives multi-agent co-evolution via Latent-Space Neural Fictitious Self-Play. Extensive experimental results demonstrate that gaming within this structured latent space substantially outperforms direct exploration. By constraining strategic exploration through a pretrained motion decoder, RoboStriker substantially reduces the catastrophic balance failures observed in raw action-space methods and achieves superior tactical performance in both competitive win rates and striking efficiency. Finally, we successfully deploy and validate our learned combat policies on real-world humanoid robots. Our code and video and supplementary materials are available at RoboStriker.
GeniWorld is presented, an interactive world model for robots that generalizes robustly across unseen scenarios by explicitly decoupling embodiment kinematics from environmental dynamics, and generates diverse manipulation trajectories within the world model, improving downstream policy performance and robustness in complex environments.
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.
Learning to execute complex, multi-stage tasks requires skill representations that are both compositionally stable and adaptive in execution. Existing hierarchical approaches often face a fundamental trade-off: continuous skills suffer from representational drift due to unconstrained embedding boundaries, while discrete skills exhibit limited expressivity because their deterministic selection cannot capture the multi-modal nuances required for adaptive execution. This tension makes it difficult to achieve reliable composition and adaptive control within a single framework. To address this, we propose DualSkill, a hierarchical framework that learns stable hard skill primitives and builds adaptive soft skills from them. Specifically, DualSkill acquires discrete hard skills via vector quantization with motion-aware distillation, yielding robust and reusable motion primitives that provide structural anchors for skill composition. Conditioned on these primitives, soft skills are modeled as probabilistic continuous mixtures that adapt skill execution while preserving temporal consistency. DualSkill then predicts future skill intentions autoregressively and decodes them into precise low-level actions. We support DualSkill with both theoretical guarantees on its skill representation and extensive experiments across diverse simulation benchmarks and a real-world robotic platform, showing that it outperforms strong baselines and improves generalization. Note to Practitioners—This paper was motivated by the need for robots to execute complex, multi-step tasks in dynamic environments such as homes, warehouses, and factories. In practice, control systems often struggle to balance modular, reusable skills with smooth transitions, leading to unstable or inefficient behavior when task conditions change. Existing approaches typically force a trade-off: either continuous skills that suffer from representational drift or discrete libraries that result in inflexible behavior. This paper presents DualSkill, a hierarchical framework that bridges this gap by decomposing behaviors into stable hard skills for structural reliability and adaptive soft skills for smooth execution. We validate that DualSkill significantly reduces failure rates in complex manipulation tasks on both simulated benchmarks and physical robots. However, the system still relies on structured training data, which may limit its initial deployment in highly unstructured environments. In the future, DualSkill could be applied to mobile robots and human-robot collaboration, further leveraging its flexible and robust framework for real-world tasks.
Ziru Wang, Long Qian, Hao-Wen Sun et al.· IEEE Transactions on Automat...· 0 citations
Robust motion planning in dense traffic requires autonomous vehicles to interact in rare and safety-critical scenarios that are underrepresented in naturalistic driving data. Although adversarial training offers a feasible solution, existing methods often rely on external scenario generators, heuristic perturbations, or simulator-heavy rollouts, which makes them difficult to integrate with modern autoregressive planners. Here, we cast adversarially robust planner learning as a constrained min-max game and propose Adversarial World Modeling (AWM), a theoretically grounded multi-agent self-play fine-tuning framework. Since solving the exact game is intractable, AWM introduces a principled decoupled solver. In the inner minimization, the planner's predictive world model is converted into a role-conditioned adversary that learns sparse, scene-adaptive attack coalitions via counterfactual credit assignment. In the outer maximization, the ego planner optimizes a regret-aware robust best response against the frozen AWM, utilizing tail-risk weighting and reference-anchored trust regions to improve hard-case recovery while preserving nominal driving behavior. Experiments on the nuPlan and InterPlan benchmarks demonstrate that our method generates transferable adversarial interactions and yields a robust planner that achieves competitive closed-loop performance in both nominal and highly interactive long-tail scenarios. Theoretical analysis justifies the decoupled solver and the main optimization components.
Tong Nie, Yuewen Mei, Junlin He et al.· arXiv.org· 0 citations
Long-horizon humanoid loco-manipulation requires composing versatile whole-body skills and reliable high-level decision making. Existing methods often coordinate pretrained skills with scripted planners, finite-state machines or task-specific model-free policies, restricting their ability to handle complex task sequences. To address this limitation, we propose \textbf{LUCID}, a hierarchical model-based reinforcement learning framework that plans over reusable skills through imagined rollouts of a learned dynamics model. LUCID first trains a structured latent-conditioned low-level policy via adversarial imitation and then freezes it while jointly learning a high-level policy and macro-dynamics world model. The world model predicts the temporally extended state transitions induced by latent decisions, enabling high-level policy optimization through imagined rollouts. We evaluate our framework across various simulated multi-object rearrangement scenarios. Experimental results show that LUCID improves the full-task success and partial-completion rates compared to prior baseline methods, demonstrating its effectiveness in complex sequential loco-manipulation tasks.
Cheng Guo, Mingzhe Ni, A. Cangelosi et al.· 0 citations
This work revisits the scaling recipe for BFMs and demonstrates that substantial performance gains can be achieved through the coordination of three core components: the learning paradigm of motion tracking that reformulates diverse humanoid control problems as the reproduction of integrated whole-body behaviors in the global frame.
