A hierarchical tactile-aware policy trained from human demos and sim RL improves real quadrupedal loco-manipulation by 28.54% on average over vision-only and visuotactile baselines.
Locotouch: Learning dynamic quadrupedal transport with tactile sensing
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
HiPi integrates a compact readout PCB, STM32 MCU, optimized comms, and FPCB layers to deliver 220 Hz readout on 2048-taxel bimanual arrays while raising contact-geometry IoU from 0.428 to 0.797 versus a reproducible baseline.
citing papers explorer
-
Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies
A hierarchical tactile-aware policy trained from human demos and sim RL improves real quadrupedal loco-manipulation by 28.54% on average over vision-only and visuotactile baselines.
-
HiPi: Reproducible High-Fidelity Piezoresistive Sensors for Robotic Manipulation
HiPi integrates a compact readout PCB, STM32 MCU, optimized comms, and FPCB layers to deliver 220 Hz readout on 2048-taxel bimanual arrays while raising contact-geometry IoU from 0.428 to 0.797 versus a reproducible baseline.