MuseVLA adds on-demand sensor selection via tokens and converts readings into grounded sensor images for multimodal fusion, reporting 80.6% average success on real-robot dexterous tasks that need non-visual sensing.
hub Canonical reference
Forcevla: Enhancing vla models with a force-aware moe for contact-rich manipulation
Canonical reference. 86% of citing Pith papers cite this work as background.
15
Pith papers citing it
Background
86% of classified citations
hub tools
citation-role summary
background 7
citation-polarity summary
roles
background 7representative citing papers
AT-VLA proposes adaptive tactile injection and a dual-stream tactile reaction mechanism to enhance VLA models for contact-rich robotic manipulation with real-time responses.
CoRAL lets LLMs act as adaptive cost designers for motion planners while using VLM priors and online identification to handle unknown physics, achieving over 50% higher success rates than baselines in unseen contact-rich robotic scenarios.
CoRDE uses concept-prior variational distillation and LoRA-based expert pools to route diffusion models for structurally generalizable robot manipulation policies.
Pose6DAug performs 3D multi-view object swapping via temporally coherent 6D pose trajectories to augment VLA data, reporting 16.5% relative success improvement on novel objects.
Dream-Tac unifies visual and tactile signals in a world action model using contact-gated fusion and attention bias, reporting 31.7% average action accuracy gains on six manipulation tasks.
MoRI dynamically mixes RL and IL experts with variance-based switching and IL regularization to reach 97.5% success in four real-world robotic tasks while cutting human intervention by 85.8%.
ThermoAct integrates thermal imaging into VLA models via a VLM planner to enable robots to perceive physical properties like heat and improve safety over vision-only systems.
TER-DAgger improves robotic precision insertion success rates by over 37% via residual policies from edited trajectories and force-aware intervention triggers.
Stellar VLA achieves continual learning in VLA models by maintaining a growing knowledge space and routing tasks to specialized experts conditioned on semantic relations, delivering strong LIBERO benchmark results with only 1% data replay and successful real-world transfer on dual-arm hardware.
A visuo-tactile policy learning method that exploits tactile motion correlation for contact state distinction and Mixture-of-Transformers for cross-modal fusion.
OmniUMI introduces a multimodal handheld interface that synchronously records RGB, depth, trajectory, tactile, internal grasp force, and external wrench data for training diffusion policies on contact-rich robot manipulation.
This survey organizes large VLM-based VLA models for robotic manipulation into monolithic and hierarchical paradigms, reviews their integrations and datasets, and outlines future directions.
RLDX-1 outperforms frontier VLAs such as π0.5 and GR00T N1.6 on dexterous manipulation benchmarks, reaching 86.8% success on ALLEX humanoid tasks versus around 40% for the baselines.
A literature review of intelligent automation approaches using robotics, AI, and control for disassembly, inspection, sorting, and reprocessing of end-of-life electronics.
citing papers explorer
-
Redefining End-of-Life: Intelligent Automation for Electronics Remanufacturing Systems
A literature review of intelligent automation approaches using robotics, AI, and control for disassembly, inspection, sorting, and reprocessing of end-of-life electronics.