DexForesight lifts dexterous policies with implicit spatial foresight
On 11 DexJoCo tasks success rises from 49.4% to 60.5%; three real-robot tasks reach 58.3% versus 50.0%.
Stories per day · 30 days
2 RSS
Relatedrobotics7reinforcement-learning3sim-to-real3behavior-cloning2simulation2benchmark1bimanual1data-collection1dataset1embodied-ai1foundation-model1imitation-learning1leap-hand1model-predictive-control1motion-tracking1safety-filter1tactile-sensing1visuomotor-policy1
15 stories, newest first · one card per story
Stories tagged #dexterous-manipulation
LingBot-Touch learns shared tactile tokens across sensor types
Authors say the tokens plug into diffusion policies and VLA models, including pi0.5 and LingBot-VLA.
LEAP hand policies reach in-hand poses and cut object drops
Over 4,800 unseen goals, a residual filter cuts drops 75% while still reaching 91.5% of goals.
DexPort collects dexterous-hand demos without a full robot setup
Behavior cloning on more than 2,500 demos raised task success from 36% to 78% when in-the-wild data was included.
DONIC trains one policy for generalist bimanual dexterous tracking
Without finetuning, one checkpoint reaches 86.79 percent success on unseen training-object trajectories and 57.32 percent on held-out objects.
AdaDex turns short human demos into adaptable dexterous skills
One policy trains on motion tracking and noised goal environments, then transfers to real robots.
HandsOn benchmark measures cross-embodiment dexterous manipulation
The best trained multi-task policy reached 29.1% Core-50 success; untuned GPT-6 scored 5.2%.
Dexterity from Supervised Play
DSP uses supervised play to collect robot data, plan with a world model, and match RL with far less interaction.
Dex-One2Many: Learning Dexterous Manipulation from a Single Human Demonstration
Dex-One2Many learns a generalizable dexterous manipulation policy from one human video via scene-graph-guided reinforcement learning.
PointWAM: 3D World Action Modeling for Dexterous Robotic Manipulation
PointWAM forecasts 3D scene and hand trajectories, lifting dexterous robot success on DexJoCo tasks.
DexPolicy: Scheduled Exploration for Trajectory-Guided Dexterous Manipulation
DexPolicy anneals exploration for dexterous manipulation, raising target success for PPO, GRPO, and FPO.
Morphometric Imitation: From Morphology and Contact Aware Hand Retargeting to Sim-to-Real Visuomotor Policy
Morphometric Imitation turns human hand-object demos into robot policies with 89.3% zero-shot success in 300 real trials.
DexTacWAM: A Visuo-Tactile World-Action Model for Dexterous Manipulation
DexTacWAM injects fingertip touch into a video world model, scoring 70.6 versus 38.0 on dexterous tasks.
SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection
SlipSense fuses a 32x32 piezoresistive array and MEMS accelerometer to detect robotic grip slips within 23.1 ms, generalizing zero-shot across platforms.
DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imagination
DeCAL, a contact-aware dexterous vision-language-action model with visuo-tactile fusion, reports 71% average task success.