ASENA: Self-evolving Agents for Embodied Navigation
Overview
This paper introduces ASENA, an embodied agent system that enables general-purpose coding agents to program humanoid navigation, manipulation, and interaction. ASENA-VLN, a 4B monocular navigation policy, achieves state-of-the-art success rates on R2R and RxR benchmarks. Integration with coding agents improves success rates and reduces execution time, while persistent workspace evolution enhances performance through simulator feedback.
From arXiv Robotics — research abstracts
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · Reported success rate: 68.7 percent · Basis not reported · Differing source assertions
“ieves state-of-the-art success rates of 68.7% on R2R and 70.2% on RxR. When integrated with a coding agent, learned navigation improves ASENA's success rate by 11 percentage points on both agentic benchmarks and reduces execution time. Persistent workspace evolution uses simulator feedback to improve success over ten passes through recurring 100-task subsets. Success increases from 72% to 98% on R2R and from 65% to 8”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 70.2 percent · Basis not reported · Differing source assertions
“ieves state-of-the-art success rates of 68.7% on R2R and 70.2% on RxR. When integrated with a coding agent, learned navigation improves ASENA's success rate by 11 percentage points on both agentic benchmarks and reduces execution time. Persistent workspace evolution uses simulator feedback to improve success over ten passes through recurring 100-task subsets. Success increases from 72% to 98% on R2R and from 65% to 8”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 72 percent · Basis not reported · Differing source assertions
“ieves state-of-the-art success rates of 68.7% on R2R and 70.2% on RxR. When integrated with a coding agent, learned navigation improves ASENA's success rate by 11 percentage points on both agentic benchmarks and reduces execution time. Persistent workspace evolution uses simulator feedback to improve success over ten passes through recurring 100-task subsets. Success increases from 72% to 98% on R2R and from 65% to 8”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 98 percent · Basis not reported · Differing source assertions
“ieves state-of-the-art success rates of 68.7% on R2R and 70.2% on RxR. When integrated with a coding agent, learned navigation improves ASENA's success rate by 11 percentage points on both agentic benchmarks and reduces execution time. Persistent workspace evolution uses simulator feedback to improve success over ten passes through recurring 100-task subsets. Success increases from 72% to 98% on R2R and from 65% to 8”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 65 percent · Basis not reported · Differing source assertions
“ieves state-of-the-art success rates of 68.7% on R2R and 70.2% on RxR. When integrated with a coding agent, learned navigation improves ASENA's success rate by 11 percentage points on both agentic benchmarks and reduces execution time. Persistent workspace evolution uses simulator feedback to improve success over ten passes through recurring 100-task subsets. Success increases from 72% to 98% on R2R and from 65% to 8”
Exact source · revision 1Source owner not reported
Evidence dependency changes (2)
- 2026-10-30T23:01:54.989Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T23:01:37.791Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalASENA: Self-evolving Agents for Embodied Navigation
This paper introduces ASENA, an embodied agent system that enables general-purpose coding agents to program humanoid navigation, manipulation, and interaction. ASENA-VLN, a 4B monocular navigation policy, achieves state-of-the-art success rates on R2R and RxR benchmarks. Integration with coding agents improves success rates and reduces execution time, while persistent workspace evolution enhances performance through simulator feedback.
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