Tell Robot What Not to Do: A Negation Understanding Perspective
Tell Robot What Not to Do: A Negation Understanding Perspective
This research presents NegaAlign, a parameter-efficient framework that allows vision-language-action models to follow negated instructions by reshaping intermediate instruction representations and using teacher-guided alignment. The framework improves negated instruction success rates on both simulated and real-world tasks, achieving up to 88.45% success on NegaBench and 88.8% on real-world tasks.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
Reported success rate
88.45%
View original evidence
enchmark spanning 10 scenarios across five domains for systematically evaluating manipulation under negated constraints. Experiments across GR00T, $\pi_0$, and $\pi_{0.5}$ demonstrate consistent improvements in negated instruction following. With 11.6M trainable parameters, NegaAlign increases the negated-instruction success rate of $\pi_{0.5}$ from 2.60% to 88.45% on NegaBench and from 12.4% to 88.8% on real-world t
Open source S5Reported success rate
88.8%
View original evidence
enchmark spanning 10 scenarios across five domains for systematically evaluating manipulation under negated constraints. Experiments across GR00T, $\pi_0$, and $\pi_{0.5}$ demonstrate consistent improvements in negated instruction following. With 11.6M trainable parameters, NegaAlign increases the negated-instruction success rate of $\pi_{0.5}$ from 2.60% to 88.45% on NegaBench and from 12.4% to 88.8% on real-world t
Open source S5Reported success rate
2.6%
View original evidence
enchmark spanning 10 scenarios across five domains for systematically evaluating manipulation under negated constraints. Experiments across GR00T, $\pi_0$, and $\pi_{0.5}$ demonstrate consistent improvements in negated instruction following. With 11.6M trainable parameters, NegaAlign increases the negated-instruction success rate of $\pi_{0.5}$ from 2.60% to 88.45% on NegaBench and from 12.4% to 88.8% on real-world t
Open source S5Reported success rate
12.4%
View original evidence
enchmark spanning 10 scenarios across five domains for systematically evaluating manipulation under negated constraints. Experiments across GR00T, $\pi_0$, and $\pi_{0.5}$ demonstrate consistent improvements in negated instruction following. With 11.6M trainable parameters, NegaAlign increases the negated-instruction success rate of $\pi_{0.5}$ from 2.60% to 88.45% on NegaBench and from 12.4% to 88.8% on real-world t
Open source S5trainable parameters
11,600,000
View original evidence
enchmark spanning 10 scenarios across five domains for systematically evaluating manipulation under negated constraints. Experiments across GR00T, $\pi_0$, and $\pi_{0.5}$ demonstrate consistent improvements in negated instruction following. With 11.6M trainable parameters, NegaAlign increases the negated-instruction success rate of $\pi_{0.5}$ from 2.60% to 88.45% on NegaBench and from 12.4% to 88.8% on real-world t
Open source S5number of scenarios
10
View original evidence
enchmark spanning 10 scenarios across five domains for systematically evaluating manipulation under negated constraints. Experiments across GR00T, $\pi_0$, and $\pi_{0.5}$ demonstrate consistent improvements in negated instruction following. With 11.6M trainable parameters, NegaAlign increases the negated-instruction success rate of $\pi_{0.5}$ from 2.60% to 88.45% on NegaBench and from 12.4% to 88.8% on real-world t
Open source S5number of domains
5
View original evidence
enchmark spanning 10 scenarios across five domains for systematically evaluating manipulation under negated constraints. Experiments across GR00T, $\pi_0$, and $\pi_{0.5}$ demonstrate consistent improvements in negated instruction following. With 11.6M trainable parameters, NegaAlign increases the negated-instruction success rate of $\pi_{0.5}$ from 2.60% to 88.45% on NegaBench and from 12.4% to 88.8% on real-world t
Open source S5
- paper: not_reported
- code: not_reported
- weights: not_reported
- dataset: not_reported
What remains unknown
Not established in the collected evidence: Environment, Control, Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
enchmark spanning 10 scenarios across five domains for systematically evaluating manipulation under negated constraints. Experiments across GR00T, $\pi_0$, and $\pi_{0.5}$ demonstrate consistent improvements in negated instruction following. With 11.6M trainable parameters, NegaAlign increases the negated-instruction success rate of $\pi_{0.5}$ from 2.60% to 88.45% on NegaBench and from 12.4% to 88.8% on real-world t
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org