2k citations · 3.4k across the 66 of their papers we have counts for
6 papers · 1 filter
Addressing Expensive Multi-objective Games with Postponed Preference Articulation via Memetic Co-evolution
Adam Żychowski, Abhishek Gupta, Jacek Mańdziuk +1
This paper presents algorithmic and empirical contributions demonstrating that the convergence characteristics of a co-evolutionary approach to tackle Multi-Objective Games (MOGs)…
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta +4
Dexterous multi-fingered hands are extremely versatile and provide a generic way to perform a multitude of tasks in human-centric environments. However, effectively controlling the…
Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation
YuXuan Liu, Abhishek Gupta, Pieter Abbeel +1
Imitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demons…
Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
Yuan Yuan, Yew-Soon Ong, Liang Feng +7
In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be s…
Adapting Low-Cost Platforms for Robotics Research
Thommen George Karimpanal, Mohammadreza Chamanbaz, Wenzheng Li +3
Validation of robotics theory on real-world hardware platforms is important to prove the practical feasibility of algorithms. This paper discusses some of the lessons learned while…
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning
Abhishek Gupta, Coline Devin, YuXuan Liu +2
People can learn a wide range of tasks from their own experience, but can also learn from observing other creatures. This can accelerate acquisition of new skills even when the obs…