Skip to content
aiqbench takeMachine Learning

aiqbench analysis

This research introduces FIRMGrasp, a novel metric that incorporates friction volatility to predict grasp failures where traditional deterministic models fail. By leveraging Conditional Value-at-Risk (CVaR), it provides a risk-adjusted force-closure margin capable of certifying robustness against adverse surface conditions in robotic manipulation tasks. The study demonstrates significant improvements over standard baselines on real-world datasets like LEAP Hand and Allegro Hand, highlighting the necessity of probabilistic safety margins for reliable robotics.
Source coveringarXiv
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

FIRMGrasp: A Friction-Informed Risk Margin for Robust Grasp Synthesis

arXiv3 min read

arXiv:2607.25049v1 Announce Type: new Abstract: Classical grasp quality metrics assume a single deterministic friction coefficient, so they cannot predict whether a grasp retains force closure across the range of friction values the contacting surfaces may exhibit. To predict these failures, we present FIRMGrasp, a family of friction-volatility-aware grasp quality metrics grounded in the Conditional Value-at-Risk (CVaR) risk measure. Unlike standard grasp quality assessors that assume a single friction realization, our metric evaluates the force-closure margin at the CVaR-discounted mean of the adverse friction tail, yielding a risk-adjusted margin $\varepsilon^{(\beta)}$, the inscribed-ball radius of the risk-adjusted wrench space. We establish its monotonicity in the confidence level $\beta$, its differentiability in the grasp parameters, and a probabilistic closure certificate that guarantees force closure with probability at least $\beta$ whenever $\varepsilon^{(\beta)}$ is positive. Under a calibrated friction distribution, analytic evaluation shows our $\varepsilon^{(\beta)}$ metric identifies friction-sensitive grasps that the nominal Ferrari-Canny epsilon rates as high-quality, and we compare against the nominal epsilon and recent differentiable baselines. Across 1,599 LEAP Hand and Allegro Hand grasps, 53% of the grasps the nominal Ferrari-Canny margin certifies lose force closure in the adverse friction tail. On the same set, the nominal margin separates realized shake and pick success with probabilities of only 0.53 and 0.67, near chance on shake success, whereas $\varepsilon^{(\beta)}$ orders the pair correctly with probabilities of 0.63 and 0.78, respectively. In simulated lift trials with gravity enabled at an adverse friction coefficient of 0.2, grasps $\varepsilon^{(\beta)}$ certifies reach a 70% success rate under lateral pull, against 25% for grasps the nominal margin certifies but $\varepsilon^{(\beta)}$ rejects.

Abstract:Classical grasp quality metrics assume a single deterministic friction coefficient, so they cannot predict whether a grasp retains force closure across the range of friction values the contacting surfaces may exhibit. To predict these failures, we present FIRMGrasp, a family of friction-volatility-aware grasp quality metrics grounded in the Conditional Value-at-Risk (CVaR) risk measure. Unlike standard grasp quality assessors that assume a single friction realization, our metric evaluates the force-closure margin at the CVaR-discounted mean of the adverse friction tail, yielding a risk-adjusted margin $\varepsilon^{(\beta)}$, the inscribed-ball radius of the risk-adjusted wrench space. We establish its monotonicity in the confidence level $\beta$, its differentiability in the grasp parameters, and a probabilistic closure certificate that guarantees force closure with probability at least $\beta$ whenever $\varepsilon^{(\beta)}$ is positive. Under a calibrated friction distribution, analytic evaluation shows our $\varepsilon^{(\beta)}$ metric identifies friction-sensitive grasps that the nominal Ferrari-Canny epsilon rates as high-quality, and we compare against the nominal epsilon and recent differentiable baselines. Across 1,599 LEAP Hand and Allegro Hand grasps, 53% of the grasps the nominal Ferrari-Canny margin certifies lose force closure in the adverse friction tail. On the same set, the nominal margin separates realized shake and pick success with probabilities of only 0.53 and 0.67, near chance on shake success, whereas $\varepsilon^{(\beta)}$ orders the pair correctly with probabilities of 0.63 and 0.78, respectively. In simulated lift trials with gravity enabled at an adverse friction coefficient of 0.2, grasps $\varepsilon^{(\beta)}$ certifies reach a 70% success rate under lateral pull, against 25% for grasps the nominal margin certifies but $\varepsilon^{(\beta)}$ rejects.

Robotics (cs.RO); Optimization and Control (math.OC)

arXiv:2607.25049 [cs.RO]

(or arXiv:2607.25049v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2607.25049 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Full text under the publisher's open licence · 354 words

Related coverage