Monday, November 9, 2026
8 papers added this day. New papers stay for 14 days.
Tactile sensing 1
Grasping 1
Robot foundation models 4
Humanoids and mobile manipulation 2
On Agentic Manipulation Policies
November 2026
AMP1 lets a frontier AI model drive a robot arm directly by writing Python against a sensing, perception and motion SDK, with no demonstrations and no built-in grasp skills. It reaches 85.6% on RoboLab-120 against 49.2% for the best VLA, and succeeds in 104 of 108 real Franka trials against 49 for FLUX 3 Action and 30 for pi0.5.
Grasp-TAGS: Task-Aware Grasp Selection via Critic Distillation for Prehensile Loco-Manipulation
November 2026
Train one RL loco-manipulation policy across several candidate grasps on a chair, cart or bucket, then distill its critic into a small network that picks the grasp before touching the object. In simulation this raises average rearrangement success to 89.84%, from 79.87% for the best single-grasp policy.
DKWBC: Decoupling Motion Tracking and Balancing for Humanoid Whole-Body Control
November 2026
DKWBC splits humanoid whole-body control into tracking torques computed by inverse dynamics plus an RL residual that only handles balance. Trained under the same settings as SONIC, it gets higher success and lower tracking error at every data scale from 2 to 128 hours, and runs on a real Unitree G1 carrying payloads.
MDF++: Visuo-Tactile World Action Model with End-to-end Representation Learning
November 2026
A visuo-tactile world-action model that trains its camera and GelSight encoders together with a diffusion transformer, updating each encoder only through the losses for predicting the other modalities and actions. On a real car engine it screws in bolts 16/20 times, versus 10/20 for the video-based UWM.
SUGARv2: Learning Diverse Visual Humanoid Loco-Manipulation Skills from Synthesized Interaction References
November 2026
Synthesize humanoid loco-manipulation references from reusable task templates instead of teleoperation, then train camera-based policies with one shared imitation reward. Across five pickup and carrying tasks SUGARv2 reaches 91.0% success in simulation and transfers zero-shot to a real Unitree G1.
VICTR: Value-Informed In-Context Learning for Vision-Language-Action Models
November 2026
VICTR feeds a π0.5 policy the demonstration chunk at the same stage of the task, picked by a progress estimator that reads a reference demo, instead of the most visually similar chunk. On unseen real-robot tasks success rises from 6.2% for fine-tuned π0.5 (15.0% with visual retrieval) to 23.8%.
Meituan-Robotics-0: A Vision-Language-Action Foundation Model for Desktop Manipulation
November 2026
A tabletop vision-language-action model trained on 147M vision-language samples and 15,060 hours of robot data from 12 embodiments, then post-trained on a real bimanual robot with human corrections. It scores 93.6% on RoboTwin 2.0, and its rewind-and-correct data collection reaches 70% success on real Ethernet insertion.
Open-HumanWAM: An Open Recipe for Scaling Human and Robot Experience for Embodied Foundation Models
November 2026
A world-action model pretrained on a curated 50,000-hour corpus of egocentric human video, with robot success after post-training rising log-linearly in pretraining hours. On a real bimanual robot, mean success under new tablecloths and lighting rises from 2.5% without human pretraining to 67.5%.
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.