ELEVATE: Agentic Controller Acquisition from Task Failure for Quadrupedal Manip
Overview
ELEVATE is a framework that uses persistent task failures to identify and acquire missing whole-body control capabilities for quadrupedal manipulators. Starting from a single pretrained controller, it autonomously learns controllers for wall-supported elevation, supported manipulation, and wall descent across seven simulation tasks. These controllers achieve 98.4%-100% success rates in tasks beyond the initial controller's workspace and transfer to a physical robot with 18/20 successes in rack retrieval and 16/20 in floor-to-rack placement.
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.
Environment: simulation
“vation, supported manipulation, and wall descent across a sequence of seven simulation tasks. These controllers are reused in subsequent reaching and object-manipulation tasks. On the five tasks beyond the initial controller's workspace, ELEVATE achieves mean success rates of 98.4%-100% across five independent runs, while coding-agent baselines with a fixed controller library fail to complete these tasks. The synthes”
Exact source · revision 1Source owner not reported
Control: reported autonomous
“al capability, a coding agent formulates and iteratively refines a reinforcement-learning problem, specifying behavior-specific rewards, curricula, and termination conditions. Validated controllers are added to the capability library and composed with existing controllers to retry the task. Starting from a single pretrained loco-manipulation controller, ELEVATE autonomously acquires controllers for wall-supported ele”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 98.4 percent · Basis not reported · Differing source assertions
“vation, supported manipulation, and wall descent across a sequence of seven simulation tasks. These controllers are reused in subsequent reaching and object-manipulation tasks. On the five tasks beyond the initial controller's workspace, ELEVATE achieves mean success rates of 98.4%-100% across five independent runs, while coding-agent baselines with a fixed controller library fail to complete these tasks. The synthes”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 100 percent · Basis not reported · Differing source assertions
“vation, supported manipulation, and wall descent across a sequence of seven simulation tasks. These controllers are reused in subsequent reaching and object-manipulation tasks. On the five tasks beyond the initial controller's workspace, ELEVATE achieves mean success rates of 98.4%-100% across five independent runs, while coding-agent baselines with a fixed controller library fail to complete these tasks. The synthes”
Exact source · revision 1Source owner not reported
Evidence dependency changes (3)
- 2026-10-30T18:29:31.380Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T18:29:21.488Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T16:57:37.076Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalELEVATE: Agentic Controller Acquisition from Task Failure for Quadrupedal Manipulation
ELEVATE is a framework that uses persistent task failures to identify and acquire missing whole-body control capabilities for quadrupedal manipulators. Starting from a single pretrained controller, it autonomously learns controllers for wall-supported elevation, supported manipulation, and wall descent across seven simulation tasks. These controllers achieve 98.4%-100% success rates in tasks beyond the initial controller's workspace and transfer to a physical robot with 18/20 successes in rack retrieval and 16/20 in floor-to-rack placement.
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