13 citations · 20 across the 4 of their papers we have counts for
5 papers
Cortex: Consolidating Context into Parametric Memory
Jintian Zhang, Feng He, Xinnian Liang +5
Context can improve large language model performance, but these gains may be lost after context removal. Inspired by biological systems consolidation, we formulate Memory Consolida…
Learning to Recover from Plan Execution Errors during Robot Manipulation: A Neuro-symbolic Approach
Namasivayam Kalithasan, Arnav Tuli, Vishal Bindal +3
Automatically detecting and recovering from failures is an important but challenging problem for autonomous robots. Most of the recent work on learning to plan from demonstrations…
Sketch-Plan-Generalize: Learning and Planning with Neuro-Symbolic Programmatic Representations for Inductive Spatial Concepts
Namasivayam Kalithasan, Sachit Sachdeva, Himanshu Gaurav Singh +7
Effective human-robot collaboration requires the ability to learn personalized concepts from a limited number of demonstrations, while exhibiting inductive generalization, hierarch…
Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models
Daman Arora, Himanshu Gaurav Singh, Mausam
The performance of large language models (LLMs) on existing reasoning benchmarks has significantly improved over the past years. In response, we present JEEBench, a considerably mo…
Learning Neuro-symbolic Programs for Language Guided Robot Manipulation
Namasivayam Kalithasan, Himanshu Singh, Vishal Bindal +5
Given a natural language instruction and an input scene, our goal is to train a model to output a manipulation program that can be executed by the robot. Prior approaches for this…