Author name cluster

Christopher Lin

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

2 author rows

ICRA Conference 2016 Conference Paper

Guided search for task and motion plans using learned heuristics

Rohan Chitnis
Dylan Hadfield-Menell
Abhishek Gupta 0004
Siddharth Srivastava 0001
Edward Groshev
Christopher Lin
Pieter Abbeel

Tasks in mobile manipulation planning often require thousands of individual motions to complete. Such tasks require reasoning about complex goals as well as the feasibility of movements in configuration space. In discrete representations, planning complexity is exponential in the length of the plan. In mobile manipulation, parameters for an action often draw from a continuous space, so we must also cope with an infinite branching factor. Task and motion planning (TAMP) methods integrate logical search over high-level actions with geometric reasoning to address this challenge. We present an algorithm that searches the space of possible task and motion plans and uses statistical machine learning to guide the search process. Our contributions are as follows: 1) we present a complete algorithm for TAMP; 2) we present a randomized local search algorithm for plan refinement that is easily formulated as a Markov decision process (MDP); 3) we apply reinforcement learning (RL) to learn a policy for this MDP; 4) we learn from expert demonstrations to efficiently search the space of high-level task plans, given options that address different (potential) infeasibilities; and 5) we run experiments to evaluate our system in a variety of simulated domains. We show significant improvements in performance over prior work.

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AAAI Conference 2016 Conference Paper

Re-Active Learning: Active Learning with Relabeling

Christopher Lin
M Mausam
Daniel Weld

Active learning seeks to train the best classiﬁer at the lowest annotation cost by intelligently picking the best examples to label. Traditional algorithms assume there is a single annotator and disregard the possibility of requesting additional independent annotations for a previously labeled example. However, relabeling examples is important, because all annotators make mistakes — especially crowdsourced workers, who have become a common source of training data. This paper seeks to understand the difference in marginal value between decreasing the noise of the training set via relabeling and increasing the size and diversity of the (noisier) training set by labeling new examples. We use the term re-active learning to denote this generalization of active learning. We show how traditional active learning methods perform poorly at re-active learning, present new algorithms designed for this important problem, formally characterize their behavior, and empirically show that our methods effectively make this tradeoff.

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IROS Conference 2016 Conference Paper

Sequential quadratic programming for task plan optimization

Dylan Hadfield-Menell
Christopher Lin
Rohan Chitnis
Stuart Russell 0001
Pieter Abbeel

We consider the problem of refining an abstract task plan into a motion trajectory. Task and motion planning is a hard problem that is essential to long-horizon mobile manipulation. Many approaches divide the problem into two steps: a search for a task plan and task plan refinement to find a feasible trajectory. We apply sequential quadratic programming to jointly optimize over the parameters in a task plan (e. g. , trajectories, grasps, put down locations). We provide two modifications that make our formulation more suitable to task and motion planning. We show how to use movement primitives to reuse previous solutions (and so save optimization effort) without trapping the algorithm in a poor basin of attraction. We also derive an early convergence criterion that lets us quickly detect unsatisfiable constraints so we can re-initialize their variables. We present experiments in a navigation amongst movable objects domain and show substantial improvement in cost over a backtracking refinement algorithm.

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AAAI Conference 2014 Conference Paper

Signals in the Silence: Models of Implicit Feedback in a Recommendation System for Crowdsourcing

Christopher Lin
Ece Kamar
Eric Horvitz

We exploit the absence of signals as informative observations in the context of providing task recommendations in crowdsourcing. Workers on crowdsourcing platforms do not provide explicit ratings about tasks. We present methods that enable a system to leverage implicit signals about task preferences. These signals include types of tasks that have been available and have been displayed, and the number of tasks workers select and complete. In contrast to previous work, we present a general model that can represent both positive and negative implicit signals. We introduce algorithms that can learn these models without exceeding the computational complexity of existing approaches. Finally, using data from a high-throughput crowdsourcing platform, we show that reasoning about both positive and negative implicit feedback can improve the quality of task recommendations.

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AAAI Conference 2012 Conference Paper

Dynamically Switching between Synergistic Workﬂows for Crowdsourcing

Christopher Lin
Mausam Mausam
Daniel Weld

To ensure quality results from unreliable crowdsourced workers, task designers often construct complex workﬂows and aggregate worker responses from redundant runs. Frequently, they experiment with several alternative workﬂows to accomplish the task, and eventually deploy the one that achieves the best performance during early trials. Surprisingly, this seemingly natural design paradigm does not achieve the full potential of crowdsourcing. In particular, using a single workﬂow (even the best) to accomplish a task is suboptimal. We show that alternative workﬂows can compose synergistically to yield much higher quality output. We formalize the insight with a novel probabilistic graphical model. Based on this model, we design and implement AGENTHUNT, a POMDP-based controller that dynamically switches between these workﬂows to achieve higher returns on investment. Additionally, we design ofﬂine and online methods for learning model parameters. Live experiments on Amazon Mechanical Turk demonstrate the superiority of AGENTHUNT for the task of generating NLP training data, yielding up to 50% error reduction and greater net utility compared to previous methods.

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