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Partially observable markov decision processes in robotics: A survey
Noisy sensing, imperfect control, and environment changes are defining characteristics of
many real-world robot tasks. The partially observable Markov decision process (POMDP) …
many real-world robot tasks. The partially observable Markov decision process (POMDP) …
[HTML][HTML] 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 …
require varying contact with the environment, such that the robot must either implicitly or …
[HTML][HTML] Feature sensing and robotic gras** of objects with uncertain information: A review
C Wang, X Zhang, X Zang, Y Liu, G Ding, W Yin, J Zhao - Sensors, 2020 - mdpi.com
As there come to be more applications of intelligent robots, their task object is becoming
more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We …
more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We …
Interactive perception: Leveraging action in perception and perception in action
Recent approaches in robot perception follow the insight that perception is facilitated by
interaction with the environment. These approaches are subsumed under the term …
interaction with the environment. These approaches are subsumed under the term …
Shared autonomy via hindsight optimization for teleoperation and teaming
In shared autonomy, a user and autonomous system work together to achieve shared goals.
To collaborate effectively, the autonomous system must know the user's goal. As such, most …
To collaborate effectively, the autonomous system must know the user's goal. As such, most …
Shared autonomy via hindsight optimization
S Javdani, SS Srinivasa… - Robotics science and …, 2015 - pmc.ncbi.nlm.nih.gov
In shared autonomy, user input and robot autonomy are combined to control a robot to
achieve a goal. Often, the robot does not know a priori which goal the user wants to achieve …
achieve a goal. Often, the robot does not know a priori which goal the user wants to achieve …
Reactive planar non-prehensile manipulation with hybrid model predictive control
FR Hogan, A Rodriguez - The International Journal of …, 2020 - journals.sagepub.com
This article presents an offline solution and online approximation to the hybrid control
problem of planar non-prehensile manipulation. Hybrid dynamics and underactuation are …
problem of planar non-prehensile manipulation. Hybrid dynamics and underactuation are …
Towards learning hierarchical skills for multi-phase manipulation tasks
Most manipulation tasks can be decomposed into a sequence of phases, where the robot's
actions have different effects in each phase. The robot can perform actions to transition …
actions have different effects in each phase. The robot can perform actions to transition …
Learning heuristic search via imitation
M Bhardwaj, S Choudhury… - Conference on Robot …, 2017 - proceedings.mlr.press
Robotic motion planning problems are typically solved by constructing a search tree of valid
maneuvers from a start to a goal configuration. Limited onboard computation and real-time …
maneuvers from a start to a goal configuration. Limited onboard computation and real-time …
Data-driven planning via imitation learning
Robot planning is the process of selecting a sequence of actions that optimize for a task=
specific objective. For instance, the objective for a navigation task would be to find collision …
specific objective. For instance, the objective for a navigation task would be to find collision …