Rapid co-design of Buoyancy-assisted robots for Challenging Locomotion using Gaussian Evolutionary Specialists
Overview
This paper introduces Gaussian Evolutionary Specialists (GES), a reinforcement learning-based framework for co-designing legged robots. It addresses behavioral diversity collapse by maintaining specialist policies that adapt to different morphologies. Evaluated on the Buoyancy-Assisted Light Legged Unit (BALLU) robot, GES achieves superior performance in obstacle traversal and ramp walking compared to baseline methods, reducing co-design time by 67.4% for the same number of evaluations. Hardware experiments confirm that optimized designs can traverse obstacles up to 24 cm high, significantly improving upon the original design's capability.
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Event evidence and corrections
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Control: teleoperated
“cy for every candidate. Finally, teleoperated hardware experiments evaluate the optimized BALLU morphology, which traverses obstacles of up to 24 cm, compared with 8 cm on the original design.”
Exact source · revision 1Source owner not reported
Reported quantity · obstacle height gain: 5 other · Basis not reported
“Light Legged Unit (BALLU) robot for obstacle traversal and ramp walking over design spaces of up to four dimensions. GES achieves higher best design performance than Monolithic and Mixture-of-Experts baselines, with gains of 5.0-9.0 cm in obstacle height and 10.0-12.7 points in ramp grade. For the same 200 design-optimization evaluations, GES reduces wall-clock co-design time by 67.4% compared with retraining a poli”
Exact source · revision 1Source owner not reported
Reported quantity · ramp grade gain: 10 other · Basis not reported
“Light Legged Unit (BALLU) robot for obstacle traversal and ramp walking over design spaces of up to four dimensions. GES achieves higher best design performance than Monolithic and Mixture-of-Experts baselines, with gains of 5.0-9.0 cm in obstacle height and 10.0-12.7 points in ramp grade. For the same 200 design-optimization evaluations, GES reduces wall-clock co-design time by 67.4% compared with retraining a poli”
Exact source · revision 1Source owner not reported
Reported quantity · obstacle traversal height: 24 other · Basis not reported
“cy for every candidate. Finally, teleoperated hardware experiments evaluate the optimized BALLU morphology, which traverses obstacles of up to 24 cm, compared with 8 cm on the original design.”
Exact source · revision 1Source owner not reported
Reported quantity · original design obstacle height: 8 other · Basis not reported
“cy for every candidate. Finally, teleoperated hardware experiments evaluate the optimized BALLU morphology, which traverses obstacles of up to 24 cm, compared with 8 cm on the original design.”
Exact source · revision 1Source owner not reported
Reported quantity · design-optimization evaluations: 200 other · Basis not reported
“Light Legged Unit (BALLU) robot for obstacle traversal and ramp walking over design spaces of up to four dimensions. GES achieves higher best design performance than Monolithic and Mixture-of-Experts baselines, with gains of 5.0-9.0 cm in obstacle height and 10.0-12.7 points in ramp grade. For the same 200 design-optimization evaluations, GES reduces wall-clock co-design time by 67.4% compared with retraining a poli”
Exact source · revision 1Source owner not reported
Reported quantity · ramp grade gain upper bound: 10 other · Basis not reported
“Light Legged Unit (BALLU) robot for obstacle traversal and ramp walking over design spaces of up to four dimensions. GES achieves higher best design performance than Monolithic and Mixture-of-Experts baselines, with gains of 5.0-9.0 cm in obstacle height and 10.0-12.7 points in ramp grade. For the same 200 design-optimization evaluations, GES reduces wall-clock co-design time by 67.4% compared with retraining a poli”
Exact source · revision 1Source owner not reported
Reported quantity · obstacle height gain upper bound: 5 other · Basis not reported
“Light Legged Unit (BALLU) robot for obstacle traversal and ramp walking over design spaces of up to four dimensions. GES achieves higher best design performance than Monolithic and Mixture-of-Experts baselines, with gains of 5.0-9.0 cm in obstacle height and 10.0-12.7 points in ramp grade. For the same 200 design-optimization evaluations, GES reduces wall-clock co-design time by 67.4% compared with retraining a poli”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalRapid co-design of Buoyancy-assisted robots for Challenging Locomotion using Gaussian Evolutionary Specialists
This paper introduces Gaussian Evolutionary Specialists (GES), a reinforcement learning-based framework for co-designing legged robots. It addresses behavioral diversity collapse by maintaining specialist policies that adapt to different morphologies. Evaluated on the Buoyancy-Assisted Light Legged Unit (BALLU) robot, GES achieves superior performance in obstacle traversal and ramp walking compared to baseline methods, reducing co-design time by 67.4% for the same number of evaluations. Hardware experiments confirm that optimized designs can traverse obstacles up to 24 cm high, significantly improving upon the original design's capability.
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