Geometry-Aware Predictive Control for Walk-to-Run Bipedal Ob
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: Geometry-Aware Predictive Control for Bipedal Obstacle Crossing. A framework enables smooth walk-to-run transitions for bipedal robots crossing obstacles. It uses five phases and MPC to ensure clearance and stability, achieving success on four out of five obstacles.
Generated from attributed reports · Updated 2 hours ago
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
“es smooth walk-to-run transition without discrete controller switching. The generated references are tracked using MPC to regulate motion smoothness, clearance feasibility, touchdown behavior, and lateral admissibility. The framework is evaluated in reduced-order simulation over five progressively difficult obstacles under walking and running conditions. The robot successfully crosses the first four obstacles, achiev”
Exact source · revision 1Source owner not reported
Reported quantity · body-obstacle clearances: 0.036 other · Basis not reported · Differing source assertions
“ing minimum body-obstacle clearances of 0.036, 0.036, 0.059, and 0.017 m, respectively. The fifth and most demanding obstacle leads to collision, identifying the current operating boundary of the proposed reduced-order formulation. Overall, the method provides a compact, interpretable, and computationally efficient basis for intelligent obstacle-aware bipedal locomotion.”
Exact source · revision 1Source owner not reported
Reported quantity · body-obstacle clearances: 0.059 other · Basis not reported · Differing source assertions
“ing minimum body-obstacle clearances of 0.036, 0.036, 0.059, and 0.017 m, respectively. The fifth and most demanding obstacle leads to collision, identifying the current operating boundary of the proposed reduced-order formulation. Overall, the method provides a compact, interpretable, and computationally efficient basis for intelligent obstacle-aware bipedal locomotion.”
Exact source · revision 1Source owner not reported
Reported quantity · body-obstacle clearances: 0.017 other · Basis not reported · Differing source assertions
“ing minimum body-obstacle clearances of 0.036, 0.036, 0.059, and 0.017 m, respectively. The fifth and most demanding obstacle leads to collision, identifying the current operating boundary of the proposed reduced-order formulation. Overall, the method provides a compact, interpretable, and computationally efficient basis for intelligent obstacle-aware bipedal locomotion.”
Exact source · revision 1Source owner not reported
Reported quantity · obstacles: 5 other · Basis not reported
“es smooth walk-to-run transition without discrete controller switching. The generated references are tracked using MPC to regulate motion smoothness, clearance feasibility, touchdown behavior, and lateral admissibility. The framework is evaluated in reduced-order simulation over five progressively difficult obstacles under walking and running conditions. The robot successfully crosses the first four obstacles, achiev”
Exact source · revision 1Source owner not reported
Reported quantity · clearances: 0.036 other · Basis not reported · Differing source assertions
“ing minimum body-obstacle clearances of 0.036, 0.036, 0.059, and 0.017 m, respectively. The fifth and most demanding obstacle leads to collision, identifying the current operating boundary of the proposed reduced-order formulation. Overall, the method provides a compact, interpretable, and computationally efficient basis for intelligent obstacle-aware bipedal locomotion.”
Exact source · revision 1Source owner not reported
Reported quantity · clearances: 0.059 other · Basis not reported · Differing source assertions
“ing minimum body-obstacle clearances of 0.036, 0.036, 0.059, and 0.017 m, respectively. The fifth and most demanding obstacle leads to collision, identifying the current operating boundary of the proposed reduced-order formulation. Overall, the method provides a compact, interpretable, and computationally efficient basis for intelligent obstacle-aware bipedal locomotion.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsGeometry-Aware Predictive Control for Bipedal Obstacle Crossing
A framework enables smooth walk-to-run transitions for bipedal robots crossing obstacles. It uses five phases and MPC to ensure clearance and stability, achieving success on four out of five obstacles.
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