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Found 10 papers, 4 papers with code
Real-World Robot Applications of Foundation Models: A Review
This paper provides an overview of the practical application of foundation models in real-world robotics, with a primary emphasis on the replacement of specific components within existing robot systems.
Self-Recovery Prompting: Promptable General Purpose Service Robot System with Foundation Models and Self-Recovery
A general-purpose service robot (GPSR), which can execute diverse tasks in various environments, requires a system with high generalizability and adaptability to tasks and environments.
GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policy
Due to the inherent uncertainty in their deformability during motion, previous methods in deformable object manipulation, such as rope and cloth, often required hundreds of real-world demonstrations to train a manipulation policy for each object, which hinders their applications in our ever-changing world.
GenORM: Generalizable One-shot Rope Manipulation with Parameter-Aware Policy
To achieve this, we augment the policy by conditioning it on deformable rope parameters and training it with a diverse range of simulated deformable ropes so that the policy can adjust actions based on different rope parameters.
Collective Intelligence for 2D Push Manipulations with Mobile Robots
While natural systems often present collective intelligence that allows them to self-organize and adapt to changes, the equivalent is missing in most artificial systems.
World Robot Challenge 2020 -- Partner Robot: A Data-Driven Approach for Room Tidying with Mobile Manipulator
Tidying up a household environment using a mobile manipulator poses various challenges in robotics, such as adaptation to large real-world environmental variations, and safe and robust deployment in the presence of humans. The Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global competition held in September 2021, benchmarked tidying tasks in the real home environments, and importantly, tested for full system performances. For this challenge, we developed an entire household service robot system, which leverages a data-driven approach to adapt to numerous edge cases that occur during the execution, instead of classical manual pre-programmed solutions.
Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning
These results show which implementation or code details are co-adapted and co-evolved with algorithms, and which are transferable across algorithms: as examples, we identified that tanh Gaussian policy and network sizes are highly adapted to algorithmic types, while layer normalization and ELU are critical for MPO's performances but also transfer to noticeable gains in SAC.
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
Progress in deep reinforcement learning (RL) research is largely enabled by benchmark task environments.
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
We propose a novel model-based algorithm, Behavior-Regularized Model-ENsemble (BREMEN) that can effectively optimize a policy offline using 10-20 times fewer data than prior works.
Neuron as an Agent
Existing multi-agent reinforcement learning (MARL) communication methods have relied on a trusted third party (TTP) to distribute reward to agents, leaving them inapplicable in peer-to-peer environments.
