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Robotics: January 2026 Week 3

Jan 15 – Jan 21, 2026 · 71 papers analyzed · 3 breakthroughs

Summary

Week 3 (Jan 15-21): 3 breakthroughs from 71 papers. (1) 2601.09988 (UMI-FT) enables in-the-wild compliant manipulation via finger-level F/T sensing; (2) 2601.10930 introduces hierarchical RL-MPC with contact-intention interface for dexterous manipulation; (3) 2601.12428 (ReWorld) tackles the physics uncanny valley with multi-dimensional reward modeling. Dexterous manipulation and contact-rich control dominate.

Key Takeaway

The field is moving from open-loop manipulation to contact-aware, physics-grounded policies.

Breakthroughs (3)

1. In-the-Wild Compliant Manipulation with UMI-FT

Why Novel: First system providing finger-level force/torque sensing during in-the-wild demonstrations, enabling capture of both external contact and internal grasp forces.

Key Innovations:

  • CoinFT sensors on each finger for F/T during data collection
  • Adaptive Compliance Policy learns force modulation from demonstrations
  • Low-cost, portable system deployable outside lab settings

Evidence:

  • — CoinFT sensor integration and calibration
  • — Adaptive Compliance Policy architecture
  • — Force-sensitive task completion rates

Impact: Unlocks contact-aware policy learning from natural demonstrations, critical for delicate manipulation.

2. Where to Touch, How to Contact: Hierarchical RL-MPC Framework for Geometry-Aware Long-Horizon Dexterous Manipulation

Why Novel: Introduces contact-intention interface that decomposes dexterous manipulation into geometric planning (where to touch) and contact dynamics (how to contact).

Key Innovations:

  • High-level RL predicts contact points and post-contact subgoals
  • Low-level MPC handles nonsmooth contact dynamics
  • Geometry-aware representations enable long-horizon planning

Evidence:

  • — Contact-intention interface formulation
  • — Hierarchical RL-MPC architecture
  • — Long-horizon manipulation sequences

Impact: Bridges the gap between task-level planning and contact-level execution in dexterous hands.

3. ReWorld: Multi-Dimensional Reward Modeling for Embodied World Models

Why Novel: Tackles the 'physics uncanny valley' in video world models by introducing HERO, a four-headed reward model trained on 4D Embodied Preference Dataset.

Key Innovations:

  • 4D Embodied Preference Dataset with multi-dimensional annotations
  • HERO decouples physics, consistency, grounding, and task rewards
  • HERO-FPO enables tractable flow-based policy optimization

Evidence:

  • — 4D preference dataset collection methodology
  • — HERO multi-headed reward architecture
  • — Physics realism and task success improvements

Impact: Provides principled approach to training physically grounded world models for robot learning.

Trends

  • Contact-rich manipulation seeing increased attention with force sensing and contact planning

  • World models being refined with physics-aware reward functions

  • Bimanual and dual-arm systems gaining traction for complex assembly tasks

Notable Papers (4)

1. FastStair: Learning to Run Up Stairs with Humanoid Robots

GPU-parallel DCM planner with multi-stage RL for agile stair climbing.

2. A3D: Adaptive Affordance Assembly with Dual-Arm Manipulation

Per-point affordances for furniture assembly with dynamic strategy adjustment.

3. BiKC+: Bimanual Hierarchical Imitation with Keypose-Conditioned Consistency Policies

Keypose predictor + consistency model for coordinated bimanual manipulation.

4. Learning Legged MPC with Smooth Neural Surrogates

Lipschitz-controlled MLP surrogates enable stable gradient-based MPC for legged robots.

Honorable Mentions

  • X-Distill: Cross-Architecture Vision Distillation for Visuomotor Learning ()
  • ACoT-VLA: Action Chain-of-Thought for Vision-Language-Action Models ()