Public articles linked to the same research event.
arXiv The work proposes Proprioceptive Action Models (PAM), which jointly generate a compact, timing-free sketch of the robot's remaining joint-space path and a dense executable action chunk within a single transformer denoiser, with the sketch parameterized by arc length rather than time to capture geometric intent invariant to execution timing and with block-causal attention and a staggered denoising schedule maintaining directed sketch-to-action dependence; in simulation PAM improves over its action-only counterparts on Push-T and LIBERO-Long, and on four real-world bimanual tasks it raises success from 47.5% to 75.0%.
The work proposes Proprioceptive Action Models (PAM), which jointly generate a compact, timing-free sketch of the robot's remaining joint-space path and a dense executable action chunk within a single transformer denoiser, with the sketch parameterized by arc length rather than time to capture geometric intent invariant to execution timing and with block-causal attention and a staggered denoising schedule maintaining directed sketch-to-action dependence; in simulation PAM improves over its action-only counterparts on Push-T and LIBERO-Long, and on four real-world bimanual tasks it raises success from 47.5% to 75.0%.
The work proposes Proprioceptive Action Models (PAM), which jointly generate a compact, timing-free sketch of the robot's remaining joint-space path and a dense executable action chunk within a single transformer denoiser, with the sketch parameterized by arc length rather than time to capture geometric intent invariant to execution timing and with block-causal attention and a staggered denoising schedule maintaining directed sketch-to-action dependence; in simulation PAM improves over its action-only counterparts on Push-T and LIBERO-Long, and on four real-world bimanual tasks it raises success from 47.5% to 75.0%.
The work proposes Proprioceptive Action Models (PAM), which jointly generate a compact, timing-free sketch of the robot's remaining joint-space path and a dense executable action chunk within a single transformer denoiser, with the sketch parameterized by arc length rather than time to capture geometric intent invariant to execution timing and with block-causal attention and a staggered denoising schedule maintaining directed sketch-to-action dependence; in simulation PAM improves over its action-only counterparts on Push-T and LIBERO-Long, and on four real-world bimanual tasks it raises success from 47.5% to 75.0%.