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TANISH RAJ

TANISHRAJ670
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updated a Space about 1 month ago
TANISHRAJ670/vj-hyperreal-figurine-showcase
replied to lin-tan's post about 1 month ago
🚀 Excited to share that our paper, "SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models", has been accepted to #ICRA2025! 🔗 Preprint: https://arxiv.org/pdf/2409.19471 We introduce SELP (Safe Efficient LLM Planner), a novel approach for generating plans that adhere to user-specified constraints while optimizing for time-efficient execution. By leveraging linear temporal logic (LTL) to interpret natural language commands, SELP effectively handles complex commands and long-horizon tasks. 🤖 💡SELP presents three key insights: 1️⃣ Equivalence Voting: Ensures robust translations from natural language instructions into LTL specifications. 2️⃣ Constrained Decoding: Uses the generated LTL formula to guide the autoregressive inference of plans, ensuring the generated plans conform to the LTL. 3️⃣ Domain-Specific Fine-Tuning: Customizes LLMs for specific robotic tasks, boosting both safety and efficiency. 📊 Experiment: Our experiments demonstrate SELP’s effectiveness and generalizability across diverse tasks. In drone navigation, SELP outperforms state-of-the-art LLM planners by 10.8% in safety rate and by 19.8% in plan efficiency. For robot manipulation, SELP achieves a 20.4% improvement in safety rate. @yiwu @jiang719 #ICRA2025 #LLM #Robotics #Agent #LLMPlanner
replied to lin-tan's post about 1 month ago
🚀 Excited to share that our paper, "SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models", has been accepted to #ICRA2025! 🔗 Preprint: https://arxiv.org/pdf/2409.19471 We introduce SELP (Safe Efficient LLM Planner), a novel approach for generating plans that adhere to user-specified constraints while optimizing for time-efficient execution. By leveraging linear temporal logic (LTL) to interpret natural language commands, SELP effectively handles complex commands and long-horizon tasks. 🤖 💡SELP presents three key insights: 1️⃣ Equivalence Voting: Ensures robust translations from natural language instructions into LTL specifications. 2️⃣ Constrained Decoding: Uses the generated LTL formula to guide the autoregressive inference of plans, ensuring the generated plans conform to the LTL. 3️⃣ Domain-Specific Fine-Tuning: Customizes LLMs for specific robotic tasks, boosting both safety and efficiency. 📊 Experiment: Our experiments demonstrate SELP’s effectiveness and generalizability across diverse tasks. In drone navigation, SELP outperforms state-of-the-art LLM planners by 10.8% in safety rate and by 19.8% in plan efficiency. For robot manipulation, SELP achieves a 20.4% improvement in safety rate. @yiwu @jiang719 #ICRA2025 #LLM #Robotics #Agent #LLMPlanner
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