Robotics
Research on Robotics: explanations, evidence, limitations and connections between papers.
16 results
A mapless depth-image query speeds reactive drone collision checks
A GPU method queries depth images directly, reducing collision-check latency in a tested 400-query drone-planning setting.
One robot model learns to predict scenes and control varied robots
GreenWAM’s robot-specialized starting point improves RoboTwin task success over its parent under a fixed adaptation recipe.
When Robot Predictions Help Choose the Next Move
A robot may see where an object is while missing where it is moving. It may also face a changed workspace or predict a good outcome for an action that actually fails. Each problem can leave a plausible prediction with little value for the next move. The three studies suggest matching the learning evidence to the weakness: use recent history to anticipate motion, observed deployment transitions to update predicted consequences, and executed action alternatives to distinguish outcomes. Their findings come from different tests, so the answer is bounded by each study’s setting.
A driving planner organizes candidate paths by motion
ManeuverFlow groups driving paths by motion and uses shared future-scene evidence to score and refine them.
Robot models improve when actions explain intended effects
Arc adds causal traces to existing robot demonstrations and improves control across tested simulation and hardware tasks.
World-model rehearsal helps robots retain skills while learning
D2R helps robots learn sequential tasks while retaining earlier skills through regenerated world-model rehearsal.
Reflex helps a humanoid catch thrown boxes with moving-camera vision
Reflex separates body control, box-motion prediction, and vision so a humanoid can catch thrown boxes, with long-range limits.
When Robots Forget Skills, Struggle With Contact, or Have Human Motion but No Robot Commands
A robot that learns a new task may lose an old one. Another may struggle to handle an object through contact. A humanoid may have recordings of people performing a task but no matching robot commands. These problems arise at different points in learning. In the tested settings, the studies changed different points: rehearsal of earlier behavior, preparation of touch prediction, and the representation of movements learned from people. Their results suggest diagnosing the obstacle before choosing a change; each result remains tied to its own robot, tasks, and evaluation.
GLIO2 completes a route with weak LiDAR geometry
GLIO2 jointly updates LiDAR, inertial, and satellite measurements, completing a tested degenerate bridge route where LiDAR systems diverged.
Human motion helps humanoid robots follow instructions
VioLA uses shared human and robot motion targets to train humanoid control, with strong results in one laboratory evaluation.
Rewarding diverse robot successes improved tested shift averages
DRIVE rewards diverse successful robot behavior; simulation and two physical tasks show higher average success under tested shifts.
Juno connects robot predictions to motion and action learning
Juno links motion-aware robot predictions to action learning, with gains in tested simulation tasks and physical shifts.
Robot history helps most when video learning predicts forward
Robot tests find longer visual history helps after causal video pretraining, while bidirectional pretraining shows no net gain.
RealtimeWAM speeds robot action prediction while keeping success
RealtimeWAM coordinates computation in robot action models, lowering average simulation latency while keeping task success close to original inference.
Rules help frozen robot models handle new wording
A rule-based rewriter improved a frozen robot model on new instruction phrasings in simulated tasks.
Robot models learn from executed action alternatives to predict failures
CureWM repairs released robot world models with executed action alternatives, improving success–failure prediction in several tested settings.