Thursday, November 5, 2026
30 papers added this day. New papers stay for 14 days.
Tactile sensing 6
Dexterous manipulation 1
Imitation learning and data collection 6
Robot foundation models 4
RL and sim-to-real 1
Humanoids and mobile manipulation 7
Computer vision for robots 1
3D perception and scene representation 1
Planning and control 2
HRI and teleoperation 1
OopsieRecover: Learning Transferable VLA Corrections from Semantic Interventions
November 2026
OopsieRecover turns a handful of human interventions, each with a short note on why, into gated costs over 3D point sets that decide when and how to steer a frozen pi0.5 VLA. On a real DROID setup it raises task progress from 35.1% for the next-best method to 61.0%, and the corrections carry over to new object poses and scenes.
MOMASPLAT: Dynamic Novel-View Demonstration Augmentation for Mobile Manipulation
November 2026
MoMaSplat rebuilds the scene and the manipulated object as separate Gaussian splats from a mobile robot's own data, then re-renders whole demonstrations, including the object moving, from new base poses. On five real TIAGo tasks it succeeds on 37% of rollouts from unseen base poses, against 17% for the best baseline.
TactileLab: Sim-to-Real Dexterous Robot Manipulation with Efficient Shear-Sensitive Tactile Simulation
November 2026
TactileLab adds simulated tactile flow, a dense shear field computed from rigid-body contact motion in IsaacLab, so RL policies can feel tangential contact at scale. Trained only in simulation, a LEAP Hand policy using contact depth plus flow makes 1.95 real in-hand rotations in 30 s, against 0.82 with proprioception alone.
Spatial-UMI-Dream: Scaling World Action Models with Metric Geometry for UMI Manipulation
November 2026
Spatial-UMI-Dream pretrains a 5B world action model on 605 hours of UMI handheld-gripper data to predict future RGB, stereo-derived metric depth and actions together. It reaches 85.0% average success on six real bimanual tasks, 24.2 points above the best baseline (π0.5).
cuRoboV2-Fast: Fast Torque-Limited Collision-Free Motion Planning
November 2026
A GPU motion planner that optimizes B-spline trajectories with the payload in the dynamics, then retimes them under velocity, acceleration, jerk and torque limits. It plans a 3 kg-payload motion in 4.4 ms on an RTX 5090, 14.7x faster than cuRoboV2, and cuts real pick-and-drop cycle time by 34.6%.
Perceptive Humanoid Motion from Simulation
November 2026
HALO trains a Unitree G1 humanoid only on synthetic vision-language-motion data rendered in 1,000 procedurally generated rooms. Zero-shot, it sits, crouches, crawls, sidesteps through doors and pushes boxes in six unseen real homes, succeeding in 91% of 360 trials.
Reinforcement Learning Enables Scaling Generalist Robot Policy Improvement
November 2026
They post-train the pi0.5 VLA online with RL on up to 144 simulated pick tasks at once and test on held-out scenes and objects. With critic-based recipes the gains grow with the number of training tasks, up to +58 points on unseen scenes and +39 on unseen objects, while filtered fine-tuning generalizes much less.
Which Physical Tactile Cues Matter for Contact-Rich Imitation Learning? A Controlled Real-Robot Study
November 2026
From one GelSight-style sensor stream they derive tactile RGB, normal/depth maps and marker flow, and compare them as Diffusion Policy inputs over 720 real-robot trials. Touch raises success from 57.8% to 78.9%, and marker flow is the best single cue at 74.4%.
TwinBridge: Digital Twin-Driven Policy and Reward Transfer for Real-World Reinforcement Learning in Laboratory Robotics
November 2026
TwinBridge builds Gaussian-splatting digital twins of lab benches, pretrains a policy in them, and distills simulator-derived progress labels into a visual reward model for human-in-the-loop RL on the real robot. Across five lab tasks it reaches 98% mean autonomous success with at most 25 minutes of real-world interaction per task.
Online Adaptation to Latent Physical Properties for Learning from Demonstration
November 2026
A Diffusion Policy is conditioned on a latent physical-property estimate that is updated online from a learned dynamics model's prediction errors. This lets the robot regrasp or re-push objects that look identical but have different centers of mass. On a real xArm7 it raises pouch-placement success from 40.7% to 66.7% over a standard Diffusion Policy.
LumiTac: Compact, High-Rate Six-Axis Optical Tactile Sensing for Human-Robot Interaction
November 2026
A camera-free optical tactile sensor, 12 mm across and under 2 g, that reads three forces and three moments from 20 colour light channels for about USD 7. A small MLP reaches R² of 0.966-0.994 per axis at about 300 Hz, and the sensor guides a real gripper through admittance control.
Correct Robots Before They Make Mistakes: Proactive Human-in-the-Loop Intervention via Preference Learning
November 2026
An XR headset shows the operator the policy's predicted gripper path so they can take over before a mistake, and each rejected prediction is paired with the correction that followed for preference post-training. With five correction episodes, mean real-robot success over HG-DAgger rises from 44.0% to 70.3% for a diffusion policy and from 57.8% to 76.9% for X-VLA.
ELA: End-to-End Latent Action Policy Learning from Video Demonstrations
November 2026
ELA trains a latent action model, a latent-action policy and a small action decoder together, so an imitation policy can learn from human or unlabeled robot video plus a few action-labeled demos. With 10 robot demos and 40 human videos per task, it raises real-robot success from 48% (Diffusion Policy) to 70%.
Ctrl-F: Force as an Interface for Humanoid Loco-Manipulation
November 2026
Ctrl-F gives a humanoid motion tracker a 3-D force command for each hand or foot, trained without objects, plus a planner that sets those forces from the object's state. In simulation it keeps hand-task completion above 90% at 1.5x nominal object mass, where force-blind trackers drop to zero, and a real Unitree G1 pushes and kicks 20 kg boxes.
GUILD: Guided Iterative Data Collection for Imitation Learning
November 2026
GUILD tells teleoperators which tasks to collect next. A VLM proposes candidate tasks in accessible environments, and data attribution scores how much each task's demos help a hard-to-reach deployment task. In open-ended real-world collection it beats uniform collection over the same VLM proposals by about 25% average success, and beats two expert roboticists on average.
RoboTryOn: Synthesizing Dexterous Robot Demonstrations from Human Videos via Virtual Try-On
November 2026
RoboTryOn turns egocentric human videos into robot demonstrations by generating a video of the robot making the same motion and reading its actions back with an inverse dynamics model. On two bimanual robots with dexterous hands, it lifts success on seven tasks that have no robot demonstrations from 2% to 60% on average.
Asynchronous Robot Policies
November 2026
ARP splits a vision-language-action policy into two processes. A slow VLM keeps refreshing a compact memory, while a small vision-action expert reads it and acts at a much higher rate, trained to cope with stale memory. On LIBERO it keeps 81.8% success with the VLM updating at only 0.5 Hz, and when the target shifts 5 cm mid-reach it succeeds 51.5% of the time against 9.5% for a π0-style baseline.
DexHOT: Learning Hand-Object Configuration Transfer for Dexterous Manipulation
November 2026
One RL policy for a 22-DoF hand learns to move between grasps that fix both the finger joints and the object's pose in the hand, then chains these short moves into longer tasks. It succeeds on 61.5% of 200 real-robot transfers and 27 of 30 demonstration-guided task trials.
Coordinating Humanoid Loco-Manipulation through Action-Conditioned Latent Foresight
November 2026
A humanoid policy samples several whole-body and hand command sequences, predicts in latent space where each would lead, and executes the one that makes the most visual progress and is most temporally consistent. On a Unitree G1 it succeeds 85.7% on seven tasks (65.7% with unseen objects) versus 70.0% (48.6%) for GR00T N1.7.
ReflexArc: Bottom-Up Integration of Instantaneous Tactile Reflexes into Whole-Body Humanoid Control
November 2026
A full-body tactile suit on a Unitree G1 triggers whole-body withdrawal reflexes that reach the motors within 10 ms, and the learned whole-body policy is trained to keep balance without cancelling them. Compared with the same reflex delayed by 20 ms, the first force peak drops by 34.1% and the contact impulse by 2.15x.
MABEL: An Open Anthropomorphic Mobile Bimanual Platform for Teleoperation and Learning
November 2026
An open-source 56-DOF mobile bimanual robot with dexterous hands, a pitching torso, a lift and an active neck, for a US$8.1k parts cost. Apple Vision Pro head and hand tracking drives the whole body with 137 ms video latency, and an ACT policy trained through the same controller succeeds in 8 of 10 grasp-and-place trials.
FEIJOA: Task-Driven Grounding and Execution for Mobile Manipulation
November 2026
A language-model agent for mobile manipulation that grounds each instruction against a raw RGB-D scan when it arrives, and checks every skill's feasibility before running it and its effect afterwards. It resolves 94.2% of 103 open-ended grounding queries exactly (68.0% for the best prebuilt map) and completes 35 of 50 real tasks on Spot.
Robot-Matched Soft Fingers for UMI Data Collection: Lessons from 100 Hours of Pick and Place
November 2026
A battery-powered handheld UMI with soft, angled fingers that match the robot's gripper, used to collect 99.7 hours of pick and place over 53 objects. Mid-training π0.5 on this data raises success after fine-tuning on unseen objects from 35% to 57% on a Galaxea R1 Pro.
Pixel: A Co-Designed Full-Stack Robot System for Learning General-Purpose Mobile Manipulation
November 2026
Pixel is a wheeled humanoid that costs under $15,000. The robot, its teleoperation interfaces, a wearable human-demonstration rig, the whole-body controller and the policy were designed together, so human and robot data share one action space. Policies trained only on wearable-rig demonstrations reach 82.0% average task progress on three household tasks, and collecting those demonstrations is 4.2-6.2x faster than teleoperation.
Pixel-UMI: Learning Environment-Aware Whole-Body Manipulation from Sensorized Human Demonstrations
November 2026
Pixel-UMI turns head-and-gripper human demonstrations into whole-body robot training data by retargeting them against a 3D reconstruction of each scene, so the policy learns postures that clear obstacles. Across five real mobile-manipulation tasks it reaches 87.0% average task progress, and scene-aware retargeting alone adds 64 points.
TAMPAgent: Agentic Belief-Space Planning for Mobile Manipulation
November 2026
A language-model agent writes belief-space task-and-motion plans, and perception, grasp and motion tools must certify each action's constraints before the robot moves. On simulated kitchen tasks with an RB-Y1 mobile manipulator it reaches 77.2% success, against 57.2% for Code as Policies and 61.1% for Graph as Policy.
Exploring Proximity and Tactile Sensing for Robotic Manipulation
November 2026
ToFTouch puts three 4x4 multizone time-of-flight depth sensors next to an optical pressure patch on one gripper finger, so the robot can sense objects before and during contact. Closing the gripper early on ToF readings cut grasp cycle time by 7.4%, and ToF depth correction picked items 9/10 times at 2 cm depth errors where picking at the nominal depth failed every time.
ForceLDM: A Force-aware Latent Dynamics Model for Contact-Rich Manipulation
November 2026
ForceLDM distills future force/torque and optical-flow features from a privileged teacher into a π0-based student policy that sees only current observations. On five real-robot contact-rich tasks it averages 87.1% success, 14.9 points above the strongest baseline.
AnchorHEC: Targetless Hand-Eye Calibration of Wrist and Fixed Cameras
November 2026
AnchorHEC calibrates a robot's wrist camera and fixed cameras in one run from ordinary RGB images of the scene and the arm's forward kinematics, with no calibration board. On a physical xArm6 with 15 wrist poses it reaches 1.2-1.4 cm wrist-camera and 1.6-1.8 cm fixed-camera translation error, versus 2.4-10.2 cm and 3.4-8.8 cm for the JCR baseline.
HeLM: Hierarchical Explicit Language Memory for Non-Markovian Manipulation
November 2026
A fine-tuned VLM keeps a running text memory of task events, such as which buttons were already pressed, and feeds commands to a π0 low-level policy, so the robot can count, follow orders and recall information across instructions. On 10 real-robot memory tasks it reaches 85.7% subtask success within episodes (vs 72.9% for the best baseline) and 89.2% across episodes.
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.