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Michael Posa

GRASP Lab

4 papers hereh-index 212.4k citations48 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • last author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.RO4
affiliations
  • GRASP Lab
HomepageORCID 0000-0003-0599-385X
same name
  • Michael Posa — 5 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedApproximating Global Contact-Implicit MPC via Sampling and Local Complementarity

1 citations · 1 across the 1 of their papers we have counts for

collaborators

4 papers

cs.RO2026★ 1 cited

Approximating Global Contact-Implicit MPC via Sampling and Local Complementarity

Sharanya Venkatesh, Bibit Bianchini, Alp Aydinoglu +2

To achieve general-purpose dexterous manipulation, robots must rapidly devise and execute contact-rich behaviors. Existing model-based controllers are incapable of globally optimiz…

cs.RO2026

Active Tactile Exploration for Rigid Body Pose and Shape Estimation

Ethan K. Gordon, Bruke Baraki, Hien Bui +1

General robot manipulation requires the handling of previously unseen objects. Learning a physically accurate model at test time can provide significant benefits in data efficiency…

cs.RO2025

Learning a Vision-Based Footstep Planner for Hierarchical Walking Control

Minku Kim, Brian Acosta, Pratik Chaudhari +1

Bipedal robots demonstrate potential in navigating challenging terrains through dynamic ground contact. However, current frameworks often depend solely on proprioception or use man…

cs.RO2025

Perceptive Mixed-Integer Footstep Control for Underactuated Bipedal Walking on Rough Terrain

Brian Acosta, Michael Posa

Traversing rough terrain requires dynamic bipeds to stabilize themselves through foot placement without stepping in unsafe areas. Planning these footsteps online is challenging giv…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.