Friday, November 6, 2026
10 papers added this day. New papers stay for 14 days.
Tactile sensing 1
Dexterous manipulation 2
Robot foundation models 1
RL and sim-to-real 2
Humanoids and mobile manipulation 4
Latent RL Priors for Data-Efficient Dexterous Manipulation
November 2026
Distill a large-scale simulated RL policy for a KUKA arm with a dexterous hand into a frozen flow-matching action decoder, then learn only a visual front end from a few demonstrations. In simulation it beats π0.5 and GR00T N1.7 fine-tuned on the full datasets using 10x fewer demos on door lifting and 5x fewer on bimanual assembly.
Failure Prediction and Classification in Vision-Language-Action Models Via Calibrated Latent Probing
November 2026
VLA-Tug is a small LSTM probe on a VLA's backbone embeddings that predicts failures before they happen and names the likely failure mode, with an alarm threshold calibrated to bound both false alarms and missed early detections. On held-out LIBERO-X skills it raises early-detection area by 30% over the strongest learned baseline, at 0.12 ms per step.
Social Humanoid: Socially Responsive Human-Humanoid Interaction
November 2026
A Unitree G1 watches a person through an RGB-D camera and generates a whole-body reaction (handshake, high-five, hug) with a diffusion model over motion tokens shared by humans and the humanoid. On the real robot it succeeds in 90-100% of 30 trials per interaction, with 308 ms median latency.
Semantics-to-Contact: A Stagewise Framework for Robust Contact-Rich Manipulation
November 2026
S2C uses a vision-language model to bring the robot near the contact point, then learns the final force-aware insertion with human-in-the-loop residual RL on the real robot, trained on wrist images with randomized backgrounds. Under new tablecloths and lighting it averages 69-84% success on six tasks, while HIL-SERL and HIL-ConRFT mostly fail.
Playing Soccer Like Humans Do: Learning Versatile and Agile Humanoid Soccer via Latent Motion Priors
November 2026
SoccerBot trains one skill-conditioned RL policy that moves, dribbles, traps and kicks a ball on a Unitree G1, acting as a residual on a latent motion prior distilled from human soccer motion capture. In simulation it kicks successfully 97.0% of the time and dribbles 89.9%, and it transfers zero-shot to the real robot with motion-capture ball tracking.
PACT: Pose-Robust Actuation Co-Design for Tendon-Driven Robotic Hands
November 2026
PACT jointly picks tendon routing, moment arms and capstan sizes for a tendon-driven finger, so that one design covers the required fingertip forces even at its weakest pose while keeping 5 Hz joint bandwidth. For a human-sized four-DoF finger, it raises worst-pose task-force coverage from 0.24 to 0.88 over a hand-designed baseline. On the built prototype, measured force falls about 24.5% short of prediction because of tendon friction.
GSBR: Geometric Support Behavior Regularization for Online RL
November 2026
GSBR fine-tunes a frozen imitation-learned base policy with online RL, penalizing actions only by how far they fall outside the base policy's estimated support. Across 19 simulated tasks on four benchmarks it raises average success from 33% for the base policy to 95%, with about 23% higher normalized throughput than the next-best RL method.
HLO-AMP: Hierarchical Loco-Manipulation from Object-Aware Motion Priors
November 2026
A Unitree G1 learns bimanual box pick-and-place from a low-level controller trained to imitate human motion capture with the box in the simulation, steered by a high-level RL policy. One deployed policy carries boxes up to 7 kg and recovers from drops. In simulation it succeeds 80.7% of the time, vs 50.3% for a whole-body controller trained without the object.
ReContact: Reconstruct and Retarget Human Contacts from Video for Contact-Rich Humanoid Manipulation
November 2026
ReContact turns a human video into an object-centric contact plan (which body part touches which point of the object, pressing in which direction) and trains a humanoid policy rewarded for following it. In simulation it completes a thigh-supported box regrasp into a two-palm bottom hold in 83.5% of episodes versus at most 0.16% for adapted baselines, and it runs nine tasks on a real Unitree G1.
Visuo-tactile Diffusion Policy for Robotic Manipulation with Auxiliary Tactile Dynamics Prediction
November 2026
A visuotactile diffusion policy for simulated UR5 assembly, with a training-only head that predicts how the fingertip images change two steps ahead. The full model reaches 59.0% success against 42.7% for vision only, while adding touch without the auxiliary loss drops to 33.7%.
Summaries are written automatically from each paper's text. Figures and videos come from the paper and its project page; all credit goes to the authors.