HITL-D: Human-In-The-Loop Diffusion for Shared Control in Robotic Manipulation
HITL-D is a shared control framework that combines diffusion-based policies with human teleoperation for robotic manipulation tasks. The system autonomously updates end-effector orientation conditioned on scene point clouds and Cartesian position, reducing the number of joystick axes operators must manage. A 12-participant user study found 40% faster task completion, 37% lower perceived workload, and improved subjective ratings versus traditional teleoperation. The work addresses a relatively unexplored intersection of diffusion policy methods and human-in-the-loop control.
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HANDOFF: Unified humanoid whole-body controller distilled from complementary specialist teachers
HANDOFF is a single whole-body controller for humanoid robots that uses a compact, explicit command-space interface bridging task planning and motor control. It is trained via multi-teacher KL distillation into a mixture-of-experts student from three specialists: whole-body motion tracking, locomotion, and fall-recovery. Evaluated on the Unitree G1, it matches state-of-the-art velocity tracking and demonstrates natural-language-driven task execution via a VLM-based agentic planner without task-specific fine-tuning. The work is relevant to the AI/robotics intersection as it shows a practical path to deploying language-driven agentic planners on physical humanoid hardware.
AHA-WAM: Asynchronous world-action modeling with temporal decoupling for robot manipulation
AHA-WAM introduces a dual Diffusion Transformer architecture that decouples world prediction (low-frequency) from action execution (high-frequency) in robot manipulation policies, addressing the inefficiency of existing world-action models that force both branches to operate at the same temporal resolution. The system uses a rolling key-value memory video DiT as a long-horizon scene planner and a fast action DiT that queries layerwise latent context via joint attention, with Observation-Guided Video-Context Routing enabling asynchronous execution. On RoboTwin benchmarks, AHA-WAM achieves 92.80% average success and 78.3% on real-world tasks at 24.17 Hz, a 4.59x speedup over Fast-WAM, without robot-data pretraining.
UniIntervene: Agentic model reduces human interventions in real-world robot RL by 57%
UniIntervene is a proposed agentic intervention model for human-in-the-loop reinforcement learning (HiL-RL) that autonomously detects unproductive exploration and recovers robot policies toward high-value states, replacing the bulk of human corrections. The system uses future-conditioned action-value estimation, a temporal value-risk critic, and a goal-conditioned recovery policy drawing from a memory of past interventions. Experiments on real-world robotic manipulation tasks show a 57% reduction in human interventions and an 8.6% improvement in average success rate over state-of-the-art HiL-RL baselines.
Ambient Diffusion Policy: imitation learning from suboptimal robot data via noise-dependent co-training
Researchers introduce Ambient Diffusion Policy, a method for robot imitation learning that extracts useful features from suboptimal demonstrations by restricting their contribution to specific diffusion timesteps (high and low noise levels). The approach is grounded in the observation that robot action data follows a spectral power law, inducing global-to-local hierarchy and locality properties in diffusion models. Evaluated across six tasks and four types of suboptimal data, it outperforms co-training baselines by up to 33% when scaled to the Open X-Embodiment dataset.
Beyond Binary: Sim-to-Real Dexterous Manipulation with Physics-Grounded Contact Representation (CoP)
Researchers introduce Center-of-Pressure (CoP), a tactile representation grounded in physical principles designed to bridge the sim-to-real gap in contact-rich dexterous manipulation. CoP preserves dense contact information while remaining robust for sim-to-real transfer, supported by a differentiable-dynamics-based sensor calibration scheme that estimates taxel orientations without ground-truth force measurements. Evaluated on peg-in-hole insertion and ball balancing tasks, CoP-conditioned policies achieve zero-shot sim-to-real transfer on a multi-fingered robotic hand, outperforming binary-contact and raw-taxel baselines. An emergent finding is that CoP-conditioned policies implicitly encode task-relevant physical properties such as object mass.
DynaFLIP: Dynamics-Aware Multimodal Pre-Training for Robot Manipulation Perception
DynaFLIP is a pre-training framework that injects motion understanding into visual encoders for robot manipulation by constructing image-language-3D flow triplets from human and robot videos. The method encourages tri-modal alignment via simplex-volume minimization in a shared hyperspherical space, combined with cosine regularization and contrastive objectives. The resulting dynamics-aware visual backbone consistently outperforms baselines across diverse downstream policies including VLAs, with gains up to +22.5% in out-of-distribution scenarios. The work argues that robot generalization requires encoding how the world changes under action, not just static scene content.
DexHoldem: A Real-World Benchmark for Dexterous Embodied Agents Using Texas Hold'em Manipulation
DexHoldem is a new system-level benchmark for evaluating dexterous embodied agents on a ShadowHand robot performing Texas Hold'em card manipulation tasks. It provides 1,470 teleoperated demonstrations across 14 manipulation primitives, a physical policy benchmark, and an agentic perception benchmark for structured game-state recovery. Top performers include π₀.₅ at 61.2% task completion and Claude Opus 4.7 at 34.3% strict perception accuracy, with GPT 5.5 achieving 66.8% field-wise accuracy. The benchmark exposes gaps between isolated visual sub-capabilities and full closed-loop embodied decision-making.
Mana framework achieves zero-shot sim-to-real transfer for dexterous articulated tool manipulation
Researchers introduce Mana (Manipulation Animator), a sim-to-real framework that reframes dexterous robotic manipulation as an animation problem using a coarse-to-fine pipeline of procedurally-generated grasp keyframes, motion planning, and reinforcement learning. The system requires minimal human input (under one minute per tool) and achieves zero-shot sim-to-real transfer across four articulated tools with varying joint types and scales. The work addresses a longstanding gap in dexterous robotics where articulated tool use—requiring coordination of internal degrees of freedom and contact-rich interactions—has been underexplored relative to rigid object manipulation.