Saturday, November 14, 2026
18 papers added this day. New papers stay for 14 days.
Imitation learning and data collection 1
Robot foundation models 5
RL and sim-to-real 1
Humanoids and mobile manipulation 1
Computer vision for robots 2
3D perception and scene representation 1
Planning and control 3
HRI and teleoperation 1
Human motion prediction 1
Robot hardware and actuators 1
Brain-computer interfaces 1
SALA: Semantically Aligned Latent Actions Enable Dual-Axis Scaling of Embodied Foundation Models
November 2026
Language descriptions of atomic actions align the latent actions of unpaired human and robot videos, so egocentric human video can train the same action expert that drives the robot. On a 63-DoF robot with two dexterous hands, adding task-specific human video raises average success from 53.75% to 72.5%.
Your Vision Foundation Model Is Secretly an EEG Foundation Model
November 2026
SeeEEG draws EEG as images that a frozen DINOv3 vision transformer, never trained on EEG, can read, and learns only the display (montage, filters, contrast; at most 1,113 parameters per view). It beats seven frozen EEG foundation models in balanced accuracy on all eight datasets, with a mean rank of 1.3 against 2.9 for the best baseline.
ACE-CaP-0: Real-World Code-as-Policies in the Era of Experience
November 2026
A coding agent writes robot programs, asking for kinesthetic demonstrations and running simulated experiments only when it judges they will help, then freezes the code. On eight real manipulation tasks with a two-arm robot, the frozen programs succeed in 221 of 240 trials (92.1%) with no human or agent in the loop.
Safe and Efficient Navigation Considering Texting Pedestrians via Attention-Aware Cost-map Tuning
November 2026
A wheelchair robot classifies each oncoming pedestrian as attentive or texting from 3D skeleton keypoints and widens its cost-map safety margin around distracted ones. In a 12-person, 252-trial corridor study, people rated it significantly smoother (p=0.003) and more predictable (p=0.034) than LiDAR-only or prediction-only navigation, with no change in travel time.
JustRUA: A Controlled Study of GPT-6 Robot-Use Agents
November 2026
A controlled study of the harness that lets GPT-6 Astra drive a bimanual arm directly, testing the loop, action interface, reused experience and scene geometry one at a time. Keeping full history, short motions, predicted depth and coordinate-solving code raises the RoboDojo simulation score from 29.7 to 50.3 with no learned policy.
Embedded Sim-to-Real Visual Servoing: Learned Reaching and Force-Guided Placement
November 2026
A Jetson-driven Franka FR3 picks a block and inserts it into a shape sorter, either with two small PPO reaching policies trained in simulation or with a scripted force-guided spiral search. Over 50 real trials each, the learned hybrid completed 70% and the force-guided pipeline 74%.
What Makes a VLA Reactive? A Study of Execution Strategies for Dynamic Manipulation
November 2026
A study of what makes the π0.5 VLA react to moving objects, in simulation and on three real Franka tasks. How the policy is executed matters as much as the policy itself. On a real conveyor belt, asynchronous execution lifts success from 20% to 60-75%, and the best continuity-preserving method reaches 95%.
BiPOP: Partial-Order Planning for Long-Horizon Bimanual Robotic Manipulation
November 2026
An LLM writes a bimanual task as a PDDL problem and a temporal planner decides which arm does what and what runs in parallel. Built on non-reasoning GPT-4.1, BiPOP succeeds on 100%, 100% and 95% of three simulated task domains, against 98%, 87% and 86% for open-loop planning with o3.
ARTS: An Open-Source, Reproducible Three-Arm Platform for Autonomy Research in Minimally Invasive Surgery
November 2026
An open-source surgical robot built from purchasable parts for about USD 137,000, with two UR5e arms driving da Vinci instruments, a UR5 stereo endoscope and a haptic surgeon console. On the real arms it reaches 0.14-0.25 mm ISO 9283 repeatability and a 58 ms hand-to-arm latency.
Does Your World Model Know Actions Have Consequences?
November 2026
An Action Perturbation (AP) metric scores whether a world model predicts how the state changes when an action is perturbed, which state error does not capture. With TD-MPC2 on two simulated tasks, a dynamics head trained on noise-injected demonstrations has state error similar to one trained on clean demonstrations but predicts action sensitivity about 16% (Push) and 48% (Peg) better.
Fast Inference-Time Policy Refinement via Action-Affine World Models
November 2026
A2WM refines a frozen diffusion policy at test time with a learned world model whose predictions are affine in action corrections, so each control step is one model call plus a small convex QP. On three simulated FurnitureBench assembly tasks and PushT it raises average success from 27.2% to 59.5%, adding 3.8 ms per step.
From a Single RGB Image to Robot Execution: Metric Scene Alignment and Contact-Guided Retargeting
November 2026
Turns one RGB photo of a scene into a reference motion that a Unitree G1 can execute. The pipeline reconstructs the scene, generates a human interaction in it, and fixes contact errors both before and after retargeting to the robot. It succeeds in 89% of simulated trials and 35 of 50 real-robot trials on boxes, steps, gaps and slopes.
Adapting Monocular Depth Estimation in Novel Scenes through Disparity Refinement and Robot Ego-Motion
November 2026
EM-ViT turns a frozen Depth Anything V2 relative-depth map into metric depth by training, online on a Jetson Orin, a small head that refines disparity and predicts one "virtual stereo baseline", supervised by sparse LiDAR and robot odometry. Across garage, citrus-farm and KITTI sequences it cuts mean depth error within 10 m by 32% versus online fine-tuning of metric DAV2, with 16% less training time.
EgoHMP: Achieving Precise Human Motion Prediction in 3D Scenes via Egocentric Cues
November 2026
EgoHMP predicts where a person will go in a 3D scene and how their body will move, using their head-camera video and head motion in place of eye gaze. On GIMO it cuts trajectory error from 575 to 481 mm and pose error from 141 to 115 mm compared with the best gaze-based method.
Be My Eye: Learning to See Beyond a Robot's Own Perspective
November 2026
Two robots each compress what their cameras see into 32 task-conditioned tokens and feed them into the partner's pi0.5 policy. On real Astribot S1 robots, choosing a box that only the partner saw being marked rises from 38.9% to 88.9% accuracy, ahead of sharing 160 generic SigLIP tokens (72.2%).
SPARK: Simple Post-training for Adapting pRetrained Knowledge to Robot Control
November 2026
SPARK post-trains off-the-shelf vision encoders (CLIP, SigLIP, DINOv2, Theia) on ordinary video so their class token captures how a scene changes, while patch features stay close to the original model. Frozen SPARK features raise average imitation-learning scores by 4.0-5.3 points across MetaWorld, DMC and Franka Kitchen, and success by 5-10 points on three real-robot tasks.
CSG-Nav: Confidence-Aware Semantic-Geometric Fusion for Robust Exploration and Accurate Zero-Shot Object-Goal Navigation
November 2026
CSG-Nav finds named objects in unseen buildings without task-specific training. It fuses YOLO-World detections, VLM scene descriptions and LLM scoring into a distance-weighted map, and stops only once depth places the target within 1 m. With 4B-parameter models it reaches 58.9% and 71.6% success on HM3Dv1 and HM3Dv2, and 25 of 50 real-robot trials.
Reliability-Adaptive Multimodal Relative Pose Tracking for Underwater Leader-Follower Robots
November 2026
A follower underwater robot tracks the 6-DoF pose of a moving leader by fusing a camera, ultrasonic ranging and a pressure-sensor "lateral line" that feels the leader's wake, weighting each by its current reliability. Over 648 pool trials it averages 3.8 cm position and 2.4° orientation error, and it runs in 17.62 ms on a Jetson AGX Orin for closed-loop formation keeping.
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.