# Ian Chuang and coauthors report dexterous in-hand pose reaching with fewer object drops [Canonical reading](https://escapemeridian.com/readings/ian-chuang-and-coauthors-report-dexterous-in-hand-pose-reaching-b3aeabb6) In their arXiv paper, Ian Chuang and coauthors describe training a LEAP hand in simulation to move an object to varied positions and orientations without human data. They report transferring the learned policies to a real hand, using motion capture or a single wrist-mounted depth camera. In tests over 4,800 unseen goals across 4 objects, the state-based policy reached 95.2% of goals within the authors’ stated position and orientation tolerances. A learned residual safety filter reduced object drops by 75% while reaching 91.5% of goals. The results suggest a way to study the trade-off between task performance and avoiding drops in dexterous robot manipulation; they do not establish how the approach performs beyond the reported evaluation. The abstract does not say whether the filter’s drop reduction holds over longer use or with objects and conditions beyond those tested. How does the filter’s performance change in longer real-robot use and across a wider range of objects and conditions? Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source arXiv eess.SY · Article Original source date: Oct 27, 2026 (2026-10-27). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-06T13:46:15.591Z. First observed: 2026-10-28T04:24:46.275Z. Retrieved: 2026-10-29T21:18:46.774Z. - [Pushing Dexterity and Safety for In-Hand Object Pose Reaching That Works Remarkably Well](https://arxiv.org/abs/2610.37652) ## Limits The abstract reports 13.3 hours of real-robot evaluation across 4,800 unseen goals, 4 objects, and diverse wrist orientations; it does not establish performance over longer use or across a wider range of objects and conditions. The evaluation results and claims are reported by the paper’s authors; the supplied evidence does not include independent replication. ## Open question How does the filter’s performance change in longer real-robot use and across a wider range of objects and conditions? ## Source-stated The authors trained policies for in-hand object pose reaching in simulation without human data and transferred them to a real LEAP hand. > We present a recipe for training such policies for a LEAP hand in simulation without human data - [Supporting source](https://arxiv.org/abs/2610.37652) ## Source-stated The authors report that their state-based policy reached 95.2% of goals within specified position and orientation tolerances in real-robot evaluation. > the state-based policy reaches 95.2% of goals within 2 cm and 0.4 rad - [Supporting source](https://arxiv.org/abs/2610.37652) ## Source-stated The authors report that the learned residual safety filter reduced drops while retaining a high goal-reaching rate. > the filter cuts its drops by 75% while still reaching 91.5% of goals - [Supporting source](https://arxiv.org/abs/2610.37652) ## Escape Meridian interpretation The reported results suggest a way to examine the trade-off between reaching goals and avoiding drops in dexterous manipulation; broader performance is not established by this abstract. > letting us tune the balance between performance and safety and greatly reduce object drops - [Supporting source](https://arxiv.org/abs/2610.37652) ## Source and contributor context Chuang, Ian, Shah, Hiya, Chilamkur, Vamsi, Soltani, Iman, Tomlin, Claire · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 16b43907-c693-4409-8bc5-d74b9fa94bcf. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-06T13:46:15.591Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 7d91f045163e57a7caa205e119b63fde0e67d7a48ec5e0725d185907bb573bb9. Source text hash: ead4d49b739948cbd18c62c50c24dacc70add89da7c27c72495ed0dfb9485315. ## Revision and source-check history ### Version 1 — 2026-11-06T13:46:15.591Z In their arXiv paper, Ian Chuang and coauthors describe training a LEAP hand in simulation to move an object to varied positions and orientations without human data. They report transferring the learned policies to a real hand, using motion capture or a single wrist-mounted depth camera. In tests over 4,800 unseen goals across 4 objects, the state-based policy reached 95.2% of goals within the authors’ stated position and orientation tolerances. A learned residual safety filter reduced object drops by 75% while reaching 91.5% of goals. The results suggest a way to study the trade-off between task performance and avoiding drops in dexterous robot manipulation; they do not establish how the approach performs beyond the reported evaluation. The abstract does not say whether the filter’s drop reduction holds over longer use or with objects and conditions beyond those tested. How does the filter’s performance change in longer real-robot use and across a wider range of objects and conditions? ## Original source arXiv eess.SY · Article Original source date: Oct 27, 2026 (2026-10-27). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-06T13:46:15.591Z. First observed: 2026-10-28T04:24:46.275Z. Retrieved: 2026-10-29T21:18:46.774Z. - [Pushing Dexterity and Safety for In-Hand Object Pose Reaching That Works Remarkably Well](https://arxiv.org/abs/2610.37652) ## Limits The abstract reports 13.3 hours of real-robot evaluation across 4,800 unseen goals, 4 objects, and diverse wrist orientations; it does not establish performance over longer use or across a wider range of objects and conditions. The evaluation results and claims are reported by the paper’s authors; the supplied evidence does not include independent replication. ## Open question How does the filter’s performance change in longer real-robot use and across a wider range of objects and conditions? ## Source-stated The authors trained policies for in-hand object pose reaching in simulation without human data and transferred them to a real LEAP hand. > We present a recipe for training such policies for a LEAP hand in simulation without human data - [Supporting source](https://arxiv.org/abs/2610.37652) ## Source-stated The authors report that their state-based policy reached 95.2% of goals within specified position and orientation tolerances in real-robot evaluation. > the state-based policy reaches 95.2% of goals within 2 cm and 0.4 rad - [Supporting source](https://arxiv.org/abs/2610.37652) ## Source-stated The authors report that the learned residual safety filter reduced drops while retaining a high goal-reaching rate. > the filter cuts its drops by 75% while still reaching 91.5% of goals - [Supporting source](https://arxiv.org/abs/2610.37652) ## Escape Meridian interpretation The reported results suggest a way to examine the trade-off between reaching goals and avoiding drops in dexterous manipulation; broader performance is not established by this abstract. > letting us tune the balance between performance and safety and greatly reduce object drops - [Supporting source](https://arxiv.org/abs/2610.37652) ## Source and contributor context Chuang, Ian, Shah, Hiya, Chilamkur, Vamsi, Soltani, Iman, Tomlin, Claire · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 16b43907-c693-4409-8bc5-d74b9fa94bcf. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-06T13:46:15.591Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 7d91f045163e57a7caa205e119b63fde0e67d7a48ec5e0725d185907bb573bb9. Source text hash: ead4d49b739948cbd18c62c50c24dacc70add89da7c27c72495ed0dfb9485315. ### uncertain — 2026-11-06T13:46:15.591Z — anthropic/claude-opus-5.5 Publication: 16b43907-c693-4409-8bc5-d74b9fa94bcf attribution — pass: The draft attributes the work and results to Ian Chuang and coauthors in an arXiv paper, which matches the listed author line. Performance figures are framed as author reports rather than established facts. > Authors:Ian Chuang, Hiya Shah, Vamsi Chilamkur, Iman Soltani, Claire Tomlin summary — uncertain: The facts are accurate, but central terms are not explained for a general reader: LEAP hand (a multi-fingered robot hand), policy, state-based policy, and learned residual safety filter. The summary also omits that the 95.2% and 91.5% results came from real-robot evaluation, so a reader could assume they are simulation results. The authors' 'remarkably capable and robust' framing, the lightbulb-screwing portable skill and the release of the recipe as a platform are dropped. Smallest repair: add plain glosses or tooltips, state 'real-robot' in the results sentence, and briefly mention the demonstrated uses and released recipe. > In 13.3 hours of real-robot evaluation over 4,800 unseen goals across 4 objects and diverse wrist orientations date — pass: The originalPublished date of 2026-10-27 matches the arXiv v1 submission date. The feed date of Oct 28 is later and correctly not used. > \[Submitted on 27 Oct 2026\] support — pass: Each claim matches the abstract. The interpretation about studying the trade-off between performance and safety is labeled as interpretation and stays close to the authors' own statement. The limitations and question do not overreach, and the draft notes that broader performance and independent replication are not established. > letting us tune the balance between performance and safety and greatly reduce object drops media — pass: The selected media is the verified arXiv abstract page for this paper, which is the direct original source. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/b3aeabb61aec501a0d1f7225f880a777948cd5aa91206f57b97f8c2b23b9891b/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/ian-chuang-and-coauthors-report-dexterous-in-hand-pose-reaching-b3aeabb6.json)