10,709 research outputs found
DoorGym: A Scalable Door Opening Environment And Baseline Agent
In order to practically implement the door opening task, a policy ought to be
robust to a wide distribution of door types and environment settings.
Reinforcement Learning (RL) with Domain Randomization (DR) is a promising
technique to enforce policy generalization, however, there are only a few
accessible training environments that are inherently designed to train agents
in domain randomized environments. We introduce DoorGym, an open-source door
opening simulation framework designed to utilize domain randomization to train
a stable policy. We intend for our environment to lie at the intersection of
domain transfer, practical tasks, and realism. We also provide baseline
Proximal Policy Optimization and Soft Actor-Critic implementations, which
achieves success rates between 0% up to 95% for opening various types of doors
in this environment. Moreover, the real-world transfer experiment shows the
trained policy is able to work in the real world. Environment kit available
here: https://github.com/PSVL/DoorGym/Comment: Full version (Real world transfer experiments result
A survey of robot manipulation in contact
In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the topics of (1) performing tasks that always require contact and (2) mitigating uncertainty by leveraging the environment in tasks that, under perfect information, could be performed without contact. The recent trends have seen robots perform tasks earlier left for humans, such as massage, and in the classical tasks, such as peg-in-hole, there is a more efficient generalization to other similar tasks, better error tolerance, and faster planning or learning of the tasks. Thus, in this survey we cover the current stage of robots performing such tasks, starting from surveying all the different in-contact tasks robots can perform, observing how these tasks are controlled and represented, and finally presenting the learning and planning of the skills required to complete these tasks
A review on reinforcement learning for contact-rich robotic manipulation tasks
Research and application of reinforcement learning in robotics for contact-rich manipulation tasks have exploded in recent years. Its ability to cope with unstructured environments and accomplish hard-to-engineer behaviors has led reinforcement learning agents to be increasingly applied in real-life scenarios. However, there is still a long way ahead for reinforcement learning to become a core element in industrial applications. This paper examines the landscape of reinforcement learning and reviews advances in its application in contact-rich tasks from 2017 to the present. The analysis investigates the main research for the most commonly selected tasks for testing reinforcement learning algorithms in both rigid and deformable object manipulation. Additionally, the trends around reinforcement learning associated with serial manipulators are explored as well as the various technological challenges that this machine learning control technique currently presents. Lastly, based on the state-of-the-art and the commonalities among the studies, a framework relating the main concepts of reinforcement learning in contact-rich manipulation tasks is proposed. The final goal of this review is to support the robotics community in future development of systems commanded by reinforcement learning, discuss the main challenges of this technology and suggest future research directions in the domain
EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought
Embodied AI is a crucial frontier in robotics, capable of planning and
executing action sequences for robots to accomplish long-horizon tasks in
physical environments. In this work, we introduce EmbodiedGPT, an end-to-end
multi-modal foundation model for embodied AI, empowering embodied agents with
multi-modal understanding and execution capabilities. To achieve this, we have
made the following efforts: (i) We craft a large-scale embodied planning
dataset, termed EgoCOT. The dataset consists of carefully selected videos from
the Ego4D dataset, along with corresponding high-quality language instructions.
Specifically, we generate a sequence of sub-goals with the "Chain of Thoughts"
mode for effective embodied planning. (ii) We introduce an efficient training
approach to EmbodiedGPT for high-quality plan generation, by adapting a 7B
large language model (LLM) to the EgoCOT dataset via prefix tuning. (iii) We
introduce a paradigm for extracting task-related features from LLM-generated
planning queries to form a closed loop between high-level planning and
low-level control. Extensive experiments show the effectiveness of EmbodiedGPT
on embodied tasks, including embodied planning, embodied control, visual
captioning, and visual question answering. Notably, EmbodiedGPT significantly
enhances the success rate of the embodied control task by extracting more
effective features. It has achieved a remarkable 1.6 times increase in success
rate on the Franka Kitchen benchmark and a 1.3 times increase on the Meta-World
benchmark, compared to the BLIP-2 baseline fine-tuned with the Ego4D dataset
12th EASN International Conference on "Innovation in Aviation & Space for opening New Horizons"
Epoxy resins show a combination of thermal stability, good mechanical performance, and durability, which make these materials suitable for many applications in the Aerospace industry. Different types of curing agents can be utilized for curing epoxy systems. The use of aliphatic amines as curing agent is preferable over the toxic aromatic ones, though their incorporation increases the flammability of the resin. Recently, we have developed different hybrid strategies, where the sol-gel technique has been exploited in combination with two DOPO-based flame retardants and other synergists or the use of humic acid and ammonium polyphosphate to achieve non-dripping V-0 classification in UL 94 vertical flame spread tests, with low phosphorous loadings (e.g., 1-2 wt%). These strategies improved the flame retardancy of the epoxy matrix, without any detrimental impact on the mechanical and thermal properties of the composites. Finally, the formation of a hybrid silica-epoxy network accounted for the establishment of tailored interphases, due to a better dispersion of more polar additives in the hydrophobic resin
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