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Canonical reference

Can vehicle motion planning generalize to realistic long-tail scenarios? In2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 5388–5395, 2024

Canonical reference. 73% of citing Pith papers cite this work as background.

58 Pith papers citing it
Background 73% of classified citations

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background 12 method 2 other 1

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2026 52 2025 6

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representative citing papers

X-Morph: Human Motion Priors for Scalable Robot Learning Across Morphologies

cs.RO · 2026-06-29 · unverdicted · novelty 6.0

X-Morph retargets human motions to kinematically plausible references for multiple legged morphologies, trains privileged RL trackers, and distills them into deployable policies that generalize and enable teleoperation and text-conditioned generation.

ReGuide: From Test-Time Guidance to Self-Improving Diffusion Policies

cs.LG · 2026-06-27 · unverdicted · novelty 6.0

ReGuide is a self-improving framework that uses phase-conditioned guidance to generate corrective rollouts and absorbs successful ones back into diffusion policy training, yielding 1.3-7.7x success gains on Robomimic tasks.

RGB-S: Image-Aligned Tactile Saliency for Robust Dexterous Manipulation

cs.RO · 2026-06-07 · unverdicted · novelty 6.0

RGB-S projects tactile contacts onto images as force-modulated Gaussian saliency maps via kinematics and zero-initialized conditioning, raising real-world occluded dexterous manipulation success by 26.7 percentage points over implicit baselines.

HORIZON: Recoverability-Governed Curriculum for Physical-Domain Scaling

cs.RO · 2026-06-03 · unverdicted · novelty 6.0

HORIZON is a recoverability-governed checkpointed frontier curriculum for on-policy physical-domain scaling on quadruped locomotion that identifies three regularities: uneven widening, non-monotonic composition, and the necessity of joint on-policy interaction.

AFUN: Towards an Affordance Foundation Model for Functionality Understanding

cs.RO · 2026-06-01 · unverdicted · novelty 6.0 · 2 refs

AFUN predicts task-conditional functional masks and 3D post-contact motion curves from RGB-D and language, trained via a standardized multi-source data pipeline, and reports large gains over baselines on segmentation, contact prediction, and motion tasks.

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