no code implementations • 23 Mar 2023 • Dhruv Mauria Saxena, Maxim Likhachev
In particular, our motivation is to allow the robot to reason over and consider non-prehensile rearrangement actions that lead to complex robot-object and object-object interactions where multiple objects might be moved by the robot simultaneously, and objects might tilt, lean on each other, or topple.
no code implementations • 23 Mar 2023 • Dhruv Saxena, Maxim Likhachev
In this paper, we extend M4M and present Enhanced-M4M (E-M4M) -- a systematic graph search-based solver that searches over orderings of pushes for movable objects that need to be rearranged and different possible rearrangements of the scene.
1 code implementation • 24 Jan 2023 • Shohin Mukherjee, Maxim Likhachev
However, in a number of robotics domains, the action space is heterogenous in the computational effort required to evaluate the cost of an action and its outcome.
no code implementations • 27 Sep 2022 • Shivam Vats, Maxim Likhachev, Oliver Kroemer
We use our approach to learn recovery skills for door-opening and evaluate them both in simulation and on a real robot with little fine-tuning.
no code implementations • 15 Aug 2022 • Rishi Veerapaneni, Maxim Likhachev
We show how this subtle but important change can lead to substantial reductions in expansions compared to the current blocking alternative, and see that the performance is related to the information difference between the batch computed NN and fast non-NN heuristic.
no code implementations • 23 May 2022 • Rishi Veerapaneni, Tushar Kusnur, Maxim Likhachev
In this paper, we show that, contrary to prevailing CBS beliefs, a weighted cost-to-go heuristic can be used effectively alongside the conflict heuristic in two possible variants.
1 code implementation • 18 Feb 2022 • Zhongqiang Ren, Richard Zhan, Sivakumar Rathinam, Maxim Likhachev, Howie Choset
This work addresses a Multi-Objective Shortest Path Problem (MO-SPP) on a graph where the goal is to find a set of Pareto-optimal solutions from a start node to a destination in the graph.
1 code implementation • 17 Nov 2021 • Anirudh Vemula, Wen Sun, Maxim Likhachev, J. Andrew Bagnell
However, there is little prior theoretical work that explains the effectiveness of ILC even in the presence of large modeling errors, where optimal control methods using the misspecified model (MM) often perform poorly.
1 code implementation • 11 Oct 2021 • Dhruv Mauria Saxena, Tushar Kusnur, Maxim Likhachev
A fine discretisation allows for better approximations of the continuous search space, but makes the search for a solution more computationally costly.
1 code implementation • 2 Aug 2021 • Zhongqiang Ren, Sivakumar Rathinam, Maxim Likhachev, Howie Choset
This paper addresses a generalization of the well known multi-agent path finding (MAPF) problem that optimizes multiple conflicting objectives simultaneously such as travel time and path risk.
no code implementations • 2 Aug 2021 • Zhongqiang Ren, Sivakumar Rathinam, Maxim Likhachev, Howie Choset
Incremental graph search algorithms such as D* Lite reuse previous, and perhaps partial, searches to expedite subsequent path planning tasks.
no code implementations • 13 Mar 2021 • Manash Pratim Das, Anirudh Vemula, Mayank Pathak, Sandip Aine, Maxim Likhachev
In this work, we investigate how would the robot with the help of a simulator, learn to maximize the number of boxes unloaded by each action.
no code implementations • 8 Feb 2021 • Dhruv Mauria Saxena, Muhammad Suhail Saleem, Maxim Likhachev
Unfortunately, it is infeasible to query the simulator for thousands of actions that need to be evaluated in a typical planning problem as each simulation is time-consuming.
no code implementations • 29 Jan 2021 • Ramkumar Natarajan, Howie Choset, Maxim Likhachev
We introduce a framework for aggressive quadrotor trajectory generation with global reasoning capabilities that combines the best of trajectory optimization and discrete graph search.
no code implementations • 17 Nov 2020 • Shohin Mukherjee, Chris Paxton, Arsalan Mousavian, Adam Fishman, Maxim Likhachev, Dieter Fox
Zero-shot execution of unseen robotic tasks is important to allowing robots to perform a wide variety of tasks in human environments, but collecting the amounts of data necessary to train end-to-end policies in the real-world is often infeasible.
1 code implementation • 21 Sep 2020 • Anirudh Vemula, J. Andrew Bagnell, Maxim Likhachev
In this paper we propose CMAX++, an approach that leverages real-world experience to improve the quality of resulting plans over successive repetitions of a robotic task.
no code implementations • 1 Aug 2020 • Aditya Agarwal, Yupeng Han, Maxim Likhachev
We show that our approach can achieve a speedup of 100x over PERCH, as well as better accuracy than the state-of-the-art data-driven approaches on 6-DoF pose estimation without the need for annotating ground truth poses in the training data.
no code implementations • 14 Apr 2020 • Wei Du, Fahad Islam, Maxim Likhachev
We show that MRA* is bounded suboptimal with respect to the anchor resolution search space and resolution complete.
1 code implementation • 9 Mar 2020 • Anirudh Vemula, Yash Oza, J. Andrew Bagnell, Maxim Likhachev
In this paper, we propose CMAX an approach for interleaving planning and execution.
2 code implementations • 15 Sep 2019 • Dhruv Mauria Saxena, Sangjae Bae, Alireza Nakhaei, Kikuo Fujimura, Maxim Likhachev
Traditional planning and control methods could fail to find a feasible trajectory for an autonomous vehicle to execute amongst dense traffic on roads.
no code implementations • 25 Aug 2019 • Tushar Kusnur, Shohin Mukherjee, Dhruv Mauria Saxena, Tomoya Fukami, Takayuki Koyama, Oren Salzman, Maxim Likhachev
We evaluate our framework in simulation as well as the real world.
no code implementations • 19 Oct 2015 • Venkatraman Narayanan, Maxim Likhachev
In many robotic domains such as flexible automated manufacturing or personal assistance, a fundamental perception task is that of identifying and localizing objects whose 3D models are known.