エピソード

  • TAC-LOCO: Unified Whole-Body Control for Contact-Aware Locomotion and Manipulation

    TAC-LOCO: Unified Whole-Body Control for Contact-Aware Locomotion and Manipulation
    2026/07/16
    A tactile-informed whole-body loco-manipulation controller for quadrupedal robots that unifies locomotion and manipulation using tactile sensing. Enables compliant and contact-aware whole-body control.
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    22 分
  • TACTIC: Contact-Centric Control for Whole-Arm Manipulation

    TACTIC: Contact-Centric Control for Whole-Arm Manipulation
    2026/07/16
    A contact-centric whole-arm manipulation framework that conditions control on both tactile and visual inputs. Targets dexterous manipulation tasks requiring rich contact feedback.
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    45 分
  • SIEVE: Structure-Aware Data Selection for Imitation Learning with VLAs

    SIEVE: Structure-Aware Data Selection for Imitation Learning with VLAs
    2026/07/16
    SIEVE introduces structure-aware data selection specifically for imitation learning with Vision-Language-Action models, aiming to improve training efficiency and policy quality.
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    13 分
  • DexMachina: RL for Long-Horizon Bimanual Dexterous Policies

    DexMachina: RL for Long-Horizon Bimanual Dexterous Policies
    2026/07/16
    An RL algorithm that learns long-horizon bimanual dexterous policies for any robot hand from a single human demonstration, emphasizing generalization across hands, objects, and complex motions.
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    27 分
  • Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation

    Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation
    2026/07/16
    A unified platform comprising GE-Base (video diffusion model trained on 1M+ manipulation episodes), GE-Act (flow-matching action model), and GE-Sim (neural world simulator for closed-loop control). All code, models, and benchmarks are open-sourced.
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    26 分
  • RoboTTT: Test-Time Training for Visuomotor Policies

    RoboTTT: Test-Time Training for Visuomotor Policies
    2026/07/15
    Introduces test-time training (TTT) inside the policy to natively scale visuomotor context to 8K timesteps at constant inference cost, enabling one-shot imitation from human video demos, online self-recovery from errors, and long-horizon assembly tasks.
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    39 分
  • HY-Embodied-VLM-1.0: Efficient Physical-World Agents

    HY-Embodied-VLM-1.0: Efficient Physical-World Agents
    2026/07/15
    An embodied vision-language-action model with released weights, code, and paper targeting robotic manipulation and embodied AI tasks.
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    31 分
  • Learning Unified Force and Position Control for Legged Loco-Manipulation

    Learning Unified Force and Position Control for Legged Loco-Manipulation
    2026/07/15
    Introduces a unified RL policy that jointly handles force and position control on quadrupedal and humanoid robots without force sensors, enabling position+force tracking, compliant behaviors, and force-aware imitation learning for contact-rich tasks.
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    41 分