mlsanityData to 7 Oct 2026

Complex In-Hand Manipulation via Compliance-Enabled Finger Gaiting and Multi-Modal Planning

arXiv 2201.07928 ↗First posted 20 Jan 2022cs.ROcited by 21

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Abstract (arXiv)

Constraining contacts to remain fixed on an object during manipulation limits the potential workspace size, as motion is subject to the hand's kinematic topology. Finger gaiting is one way to alleviate such restraints. It allows contacts to be freely broken and remade so as to operate on different manipulation manifolds. This capability, however, has traditionally been difficult or impossible to practically realize. A finger gaiting system must simultaneously plan for and control forces on the object while maintaining stability during contact switching. This work alleviates the traditional requirement by taking advantage of system compliance, allowing the hand to more easily switch contacts while maintaining a stable grasp. Our method achieves complete SO(3) finger gaiting control of grasped objects against gravity by developing a manipulation planner that operates via orthogonal safe modes of a compliant, underactuated hand absent of tactile sensors or joint encoders. During manipulation, a low-latency 6D pose object tracker provides feedback via vision, allowing the planner to update its plan online so as to adaptively recover from trajectory deviations. The efficacy of this method is showcased by manipulating both convex and non-convex objects on a real robot. Its robustness is evaluated via perturbation rejection and long trajectory goals. To the best of the authors' knowledge, this is the first work that has autonomously achieved full SO(3) control of objects within-hand via finger gaiting and without a support surface, elucidating a valuable step towards realizing true robot in-hand manipulation capabilities.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jan 2022.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 5; 2024: 7; 2025: 4; 2026: 2.

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  1. 2022DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Realitycited by 136
  2. 2022In-Hand Object Rotation via Rapid Motor Adaptationcited by 102
  3. 2022Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapescited by 92
  4. 2023General In-Hand Object Rotation with Vision and Touchcited by 87
  5. 2023Rotating without Seeing: Towards In-hand Dexterity through Touchcited by 78
  6. 2023LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learningcited by 78
  7. 2023Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensingcited by 51
  8. 2024Hand-Object Interaction Pretraining from Videoscited by 38
  9. 2025World Models for Learning Dexterous Hand-Object Interactions from Human Videoscited by 26
  10. 2024Lessons from Learning to Spin "Pens"cited by 22
  11. 2024AnyRotate: Gravity-Invariant In-Hand Object Rotation with Sim-to-Real Touchcited by 18
  12. 2024Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensingcited by 13
9 more
  1. 2025Touch begins where vision ends: Generalizable policies for contact-rich manipulationcited by 13
  2. 2024DexTouch: Learning to Seek and Manipulate Objects with Tactile Dexteritycited by 11
  3. 2025From Simple to Complex Skills: The Case of In-Hand Object Reorientationcited by 8
  4. 2023Estimator-Coupled Reinforcement Learning for Robust Purely Tactile In-Hand Manipulationcited by 7
  5. 2024Vegetable Peeling: A Case Study in Constrained Dexterous Manipulationcited by 7
  6. 2024DemoStart: Demonstration-led auto-curriculum applied to sim-to-real with multi-fingered robotscited by 7
  7. 2025Composing Dextrous Grasping and In-hand Manipulation via Scoring with a Reinforcement Learning Criticcited by 3
  8. 2026Robust In-Hand Manipulation via Priors in Reinforcement Learning and Mechanical Designcited by 1
  9. 2026DexMani: Human-Derived Manipulability Guidance for Dexterous Rotationcited by 1