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Quadruped robot SOMA masters object stacking with advanced RL

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SOMA, a novel hierarchical reinforcement learning framework enabling quadrupedal robots to perform complex manipulation tasks with stacked objects. This system integrates explicit robot base-height control for full three-dimensional pushing strategies, allowing the robot to manipulate payloads as a unified system without sliding or tipping. SOMA utilizes a high-level policy with a Simplified Structured State Space Sequence encoder for long-horizon reasoning and a low-level policy for dynamic leg adjustments. In simulations, SOMA achieved over 90% success rates for single-object reposing and dynamic two-box stacks, and demonstrated successful zero-shot sim-to-real transfer on a Unitree Go1 robot. AI

IMPACT Enables quadrupedal robots to perform complex manipulation tasks, potentially advancing robotics in logistics and hazardous environments.

RANK_REASON The cluster contains an academic paper detailing a new robotics framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Quadruped robot SOMA masters object stacking with advanced RL

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The cluster contains an academic paper detailing a new robotics framework. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Stergios E. Bachoumas, Panagiotis Artemiadis ·

    SOMA: Stack-of-Objects Manipulation using a Quadruped Robot

    arXiv:2610.20968v1 Announce Type: cross Abstract: Executing heavy-duty physical manipulation in unpredictable environments remains a major challenge for quadrupedal robots lacking dedicated manipulators. This paper presents SOMA, a hierarchical reinforcement learning framework fo…

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