VOCALoco: Viability-Optimized Cost-aware Adaptive Locomotion
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911236402184192 |
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| author | Wu, Stanley Danesh, Mohamad H. Li, Simon Yurchyk, Hanna Abyaneh, Amin Houssaini, Anas El Meger, David Lin, Hsiu-Chin |
| author_facet | Wu, Stanley Danesh, Mohamad H. Li, Simon Yurchyk, Hanna Abyaneh, Amin Houssaini, Anas El Meger, David Lin, Hsiu-Chin |
| contents | Recent advancements in legged robot locomotion have facilitated traversal over increasingly complex terrains. Despite this progress, many existing approaches rely on end-to-end deep reinforcement learning (DRL), which poses limitations in terms of safety and interpretability, especially when generalizing to novel terrains. To overcome these challenges, we introduce VOCALoco, a modular skill-selection framework that dynamically adapts locomotion strategies based on perceptual input. Given a set of pre-trained locomotion policies, VOCALoco evaluates their viability and energy-consumption by predicting both the safety of execution and the anticipated cost of transport over a fixed planning horizon. This joint assessment enables the selection of policies that are both safe and energy-efficient, given the observed local terrain. We evaluate our approach on staircase locomotion tasks, demonstrating its performance in both simulated and real-world scenarios using a quadrupedal robot. Empirical results show that VOCALoco achieves improved robustness and safety during stair ascent and descent compared to a conventional end-to-end DRL policy |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_23997 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VOCALoco: Viability-Optimized Cost-aware Adaptive Locomotion Wu, Stanley Danesh, Mohamad H. Li, Simon Yurchyk, Hanna Abyaneh, Amin Houssaini, Anas El Meger, David Lin, Hsiu-Chin Robotics I.2.9 Recent advancements in legged robot locomotion have facilitated traversal over increasingly complex terrains. Despite this progress, many existing approaches rely on end-to-end deep reinforcement learning (DRL), which poses limitations in terms of safety and interpretability, especially when generalizing to novel terrains. To overcome these challenges, we introduce VOCALoco, a modular skill-selection framework that dynamically adapts locomotion strategies based on perceptual input. Given a set of pre-trained locomotion policies, VOCALoco evaluates their viability and energy-consumption by predicting both the safety of execution and the anticipated cost of transport over a fixed planning horizon. This joint assessment enables the selection of policies that are both safe and energy-efficient, given the observed local terrain. We evaluate our approach on staircase locomotion tasks, demonstrating its performance in both simulated and real-world scenarios using a quadrupedal robot. Empirical results show that VOCALoco achieves improved robustness and safety during stair ascent and descent compared to a conventional end-to-end DRL policy |
| title | VOCALoco: Viability-Optimized Cost-aware Adaptive Locomotion |
| topic | Robotics I.2.9 |
| url | https://arxiv.org/abs/2510.23997 |