Benoit Landry

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Benoit Landry


Benoit is currently pursuing a Ph.D. in the department of Aeronautics and Astronautics. He received a Bachelor of Science and a Master of Engineering in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (minoring in Science, Technology and Society). At MIT, Benoit conducted research on planning and control for small aerial vehicles under the supervision of Professor Russ Tedrake. He was later responsible for control systems development at 3D Robotics. Generally speaking, Benoit’s research attempts to leverage computational breakthroughs (e.g. autodiff, modern solvers, GPU’s) to address the problems of planning and control for complex robotic systems. He is particularly interested in aerial robotics and systems that make and break contact with their environments.

Awards:

  • Siebel Foundation Scholarship, 2014
  • Stanford Robotics Center Fellowship

ASL Publications

  1. H. Dai, B. Landry, and M. Pavone, “Counter-Example Guided Synthesis of Neural Network Lyapunov Functions for Piecewise Linear Systems,” in Proc. IEEE Conf. on Decision and Control, Jeju Island, Republic of Korea, 2020. (In Press)

    Abstract: We introduce an algorithm for synthesizing and verifying piecewise linear Lyapunov functions to prove global exponential stability of piecewise linear dynamical systems. The Lyapunov functions we synthesize are parameterized by feedforward neural networks with leaky ReLU activation units. To train these neural networks, we design a loss function that measures the maximal violation of the Lyapunov conditions in the state space. We show that this maximal violation can be computed by solving a mixed-integer linear program (MILP). Compared to previous learning-based approaches, our learning approach is able to certify with high precision that the learned neural network satisfies the Lyapunov conditions not only for sampled states, but over the entire state space. Moreover, compared to previous optimization-based approaches that require a pre-specified partition of the state space when synthesizing piecewise Lyapunov functions, our method can automatically search for both the partition and the Lyapunov function simultaneously. We demonstrate our algorithm on both continuous and discrete-time systems, including some for which known strategies for partitioning of the Lyapunov function would require introducing higher order Lyapunov functions.

    @inproceedings{DaiLandryEtAl2020,
      author = {Dai, H. and Landry, B. and Pavone, M.},
      title = {Counter-Example Guided Synthesis of Neural Network Lyapunov Functions for Piecewise Linear Systems},
      booktitle = {{Proc. IEEE Conf. on Decision and Control}},
      year = {2020},
      note = {In Press},
      address = {Jeju Island, Republic of Korea},
      month = dec,
      url = {http://groups.csail.mit.edu/robotics-center/public_papers/Dai20.pdf},
      keywords = {press},
      owner = {blandry},
      timestamp = {2020-11-29}
    }
    
  2. B. Landry, H. Dai, and M. Pavone, “SEAGuL: Sample Efficient Adversarially Guided Learning of Value Functions,” 2020. (Submitted)

    Abstract: Value functions are powerful abstractions broadly used across optimal control and robotics algorithms. Several lines of work have attempted to leverage trajectory optimization to learn value function approximations, usually by solving a large number of trajectory optimization problems as a means to generate training data. Even though these methods point to a promising direction, for sufficiently complex tasks, their sampling requirements can become computationally intractable. In this work, we leverage insights from adversarial learning in order to improve the sampling efficiency of a simple value function learning algorithm. We demonstrate how generating adversarial samples for this task presents a unique challenge due to the loss function that does not admit a closed form expression of the samples, but that instead requires the solution to a nonlinear optimization problem. Our key insight is that by leveraging duality theory from optimization, it is still possible to compute adversarial samples for this learning problem with virtually no computational overhead, including without having to keep track of shifting distributions of approximation errors or having to train generative models. We apply our method, named SEAGuL, to a canonical control task (balancing the acrobot) and a more challenging and highly dynamic nonlinear control task (the perching of a small glider). We demonstrate that compared to random sampling, with the same number of samples, training value function approximations using SEAGuL leads to improved generalization errors that also translate to control performance improvement.

    @inproceedings{LandryDaiEtAl2020,
      author = {Landry, B. and Dai, H. and Pavone, M.},
      title = {SEAGuL: Sample Efficient Adversarially Guided Learning of Value Functions},
      year = {2020},
      note = {Submitted},
      month = aug,
      keywords = {sub},
      owner = {blandry},
      timestamp = {2020-11-23}
    }
    
  3. B. Landry, J. Lorenzetti, Z. Manchester, and M. Pavone, “Bilevel Optimization for Planning through Contact: A Semidirect Method,” in Int. Symp. on Robotics Research, Hanoi, Vietnam, 2019.

    Abstract: Many robotics applications, from object manipulation to locomotion, require planning methods that are capable of handling the dynamics of contact. Trajectory optimization has been shown to be a viable approach that can be made to support contact dynamics. However, the current state-of-the art methods remain slow and are often difficult to get to converge. In this work, we leverage recent advances in bilevel optimization to design an algorithm capable of efficiently generating trajectories that involve making and breaking contact. We demonstrate our method’s efficiency by outperforming an alternative state-of-the-art method on a benchmark problem. We moreover demonstrate the method’s ability to design a simple periodic gait for a quadruped with 15 degrees of freedom and four contact points

    @inproceedings{LandryLorenzettiEtAl2019,
      author = {Landry, B. and Lorenzetti, J. and Manchester, Z. and Pavone, M.},
      title = {Bilevel Optimization for Planning through Contact: A Semidirect Method},
      booktitle = {{Int. Symp. on Robotics Research}},
      year = {2019},
      address = {Hanoi, Vietnam},
      month = oct,
      url = {https://arxiv.org/pdf/1906.04292.pdf},
      owner = {blandry},
      timestamp = {2020-04-13}
    }
    
  4. B. Landry, Z. Manchester, and M. Pavone, “A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization,” in Robotics: Science and Systems, Freiburg im Breisgau, Germany, 2019.

    Abstract: Many problems in modern robotics can be addressed by modeling them as bilevel optimization problems. In this work, we leverage augmented Lagrangian methods and recent advances in automatic differentiation to develop a general-purpose nonlinear optimization solver that is well suited to bilevel optimization. We then demonstrate the validity and scalability of our algorithm with two representative robotic problems, namely robust control and parameter estimation for a system involving contact. We stress the general nature of the algorithm and its potential relevance to many other problems in robotics.

    @inproceedings{LandryManchesterEtAl2019,
      author = {Landry, B. and Manchester, Z. and Pavone, M.},
      title = {A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization},
      booktitle = {{Robotics: Science and Systems}},
      year = {2019},
      address = {Freiburg im Breisgau, Germany},
      month = jun,
      url = {https://arxiv.org/pdf/1902.03319.pdf},
      owner = {blandry},
      timestamp = {2019-05-18}
    }
    
  5. J. Lorenzetti, B. Landry, S. Singh, and M. Pavone, “Reduced Order Model Predictive Control For Setpoint Tracking,” in European Control Conference, Naples, Italy, 2019.

    Abstract: Despite the success of model predictive control (MPC), its application to high-dimensional systems, such as flexible structures and coupled fluid/rigid-body systems, remains a largely open challenge due to excessive computational complexity. A promising solution approach is to leverage reduced order models for designing the model predictive controller. In this paper we present a reduced order MPC scheme that enables setpoint tracking while robustly guaranteeing constraint satisfaction for linear, discrete, time-invariant systems. Setpoint tracking is enabled by designing the MPC cost function to account for the steady-state error between the full and reduced order models. Robust constraint satisfaction is accomplished by solving (offline) a set of linear programs to provide bounds on the errors due to bounded disturbances, state estimation, and model approximation. The approach is validated on a synthetic system as well as a high-dimensional linear model of a flexible rod, obtained using finite element methods.

    @inproceedings{LorenzettiLandryEtAl2019,
      author = {Lorenzetti, J. and Landry, B. and Singh, S. and Pavone, M.},
      title = {Reduced Order Model Predictive Control For Setpoint Tracking},
      booktitle = {{European Control Conference}},
      year = {2019},
      address = {Naples, Italy},
      month = jun,
      url = {https://arxiv.org/pdf/1811.06590.pdf},
      owner = {jlorenze},
      timestamp = {2019-04-26}
    }
    
  6. P. Abtahi, B. Landry, J. J. Yang, M. Pavone, S. Follmer, and J. A. Landay, “Beyond The Force: Using Quadcopters to Appropriate Objects and the Environment for Haptics in Virtual Reality,” in ACM CHI Conf. on Human Factors in Computing Systems, Glasgow, UK, 2019.

    Abstract: Quadcopters have been used as hovering encountered-type haptic devices in virtual reality. We suggest that quadcopters can facilitate rich haptic interactions beyond force feedback by appropriating physical objects and the environment. We present HoverHaptics, an autonomous safe-to-touch quadcopter and its integration with a virtual shopping experience. HoverHaptics highlights three affordances of quadcopters that enable these rich haptic interactions: (1) dynamic positioning of passive haptics, (2) texture mapping, and (3) animating passive props. We identify inherent challenges of hovering encountered-type haptic devices, such as their limited speed, inadequate control accuracy, and safety concerns. We then detail our approach for tackling these challenges, including the use of display techniques, visuo-haptic illusions, and collision avoidance. We conclude by describing a preliminary study (n = 9) to better understand the subjective user experience when interacting with a quadcopter in virtual reality using these techniques.

    @inproceedings{AbtahiLandryEtAl2019,
      author = {Abtahi, P. and Landry, B. and Yang, J. J. and Pavone, M. and Follmer, S. and Landay, J. A.},
      title = {Beyond The Force: Using Quadcopters to Appropriate Objects and the Environment for Haptics in Virtual Reality},
      booktitle = {{ACM CHI Conf. on Human Factors in Computing Systems}},
      year = {2019},
      note = {In Press},
      address = {Glasgow, UK},
      month = may,
      url = {https://dl.acm.org/doi/10.1145/3290605.3300589},
      owner = {blandry},
      timestamp = {2020-04-13}
    }
    
  7. S. Singh, B. Landry, A. Majumdar, J.-J. E. Slotine, and M. Pavone, “Robust Feedback Motion Planning via Contraction Theory,” Int. Journal of Robotics Research, 2019. (Submitted)

    Abstract:

    @article{SinghLandryEtAl2019,
      author = {Singh, S. and Landry, B. and Majumdar, A. and Slotine, J-J. E. and Pavone, M.},
      title = {Robust Feedback Motion Planning via Contraction Theory},
      journal = {{Int. Journal of Robotics Research}},
      year = {2019},
      note = {Submitted},
      keywords = {sub},
      owner = {ssingh19},
      timestamp = {2019-09-11},
      url = {/wp-content/papercite-data/pdf/Singh.Landry.ea.IJRR19.pdf}
    }
    
  8. J. Lorenzetti, M. Chen, B. Landry, and M. Pavone, “Reach-Avoid Games Via Mixed-Integer Second-Order Cone Programming,” in Proc. IEEE Conf. on Decision and Control, Miami Beach, Florida, 2018.

    Abstract: Reach-avoid games are excellent proxies for studying many problems in robotics and related fields, with applications including multi-robot systems, human-robot interactions, and safety-critical systems. Solving reach-avoid games is however difficult due to the conflicting and asymmetric goals of agents, and trade-offs between optimality, computational complexity, and solution generality are commonly required. This paper seeks to find attacker strategies in reach-avoid games that reduce computational complexity while retaining solution quality by using a receding horizon strategy. To solve for the open-loop strategy fast enough to enable an receding horizon approach, the problem is formulated as a mixed-integer second-order cone program. This formulation leverages the use of sums-of-squares optimization to provide guarantees that the strategy is robust to all possible defender policies. The method is demonstrated through numerical and hardware experiments.

    @inproceedings{LorenzettiChenEtAl2018,
      author = {Lorenzetti, J. and Chen, M. and Landry, B. and Pavone, M.},
      title = {Reach-Avoid Games Via Mixed-Integer Second-Order Cone Programming},
      booktitle = {{Proc. IEEE Conf. on Decision and Control}},
      year = {2018},
      address = {Miami Beach, Florida},
      month = dec,
      url = {/wp-content/papercite-data/pdf/Lorenzetti.Chen.Landry.Pavone.CDC18.pdf},
      owner = {jlorenze},
      timestamp = {2019-09-25}
    }
    
  9. B. Landry, M. Chen, S. Hemley, and M. Pavone, “Reach-Avoid Problems via Sum-of-Squares Optimization and Dynamic Programming,” in IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, Madrid, Spain, 2018.

    Abstract: Reach-avoid problems involve driving a system to a set of desirable configurations while keeping it away from undesirable ones. Providing mathematical guarantees for such scenarios is challenging but have numerous potential practical applications. Due to the challenges, analysis of reach-avoid problems involves making trade-offs between generality of system dynamics, generality of problem setups, optimality of solutions, and computational complexity. In this paper, we combine sum-of-squares optimization and dynamic programming to address the reach-avoid problem, and provide a conservative solution that maintains reaching and avoidance guarantees. Our method is applicable to polynomial system dynamics and to general problem setups, and is more computationally scalable than previous related methods. Through a numerical example involving two single integrators, we validate our proposed theory and compare our method to Hamilton-Jacobi reachability. Having validated our theory, we demonstrate the computational scalability of our method by computing the reach-avoid set of a system with two kinematic cars.

    @inproceedings{LandryChenEtAl2018,
      author = {Landry, B. and Chen, M. and Hemley, S. and Pavone, M.},
      title = {Reach-Avoid Problems via Sum-of-Squares Optimization and Dynamic Programming},
      booktitle = {{IEEE/RSJ Int. Conf. on Intelligent Robots \& Systems}},
      year = {2018},
      address = {Madrid, Spain},
      month = oct,
      url = {https://arxiv.org/pdf/1807.11553.pdf},
      owner = {blandry},
      timestamp = {2018-03-03}
    }
    
  10. B. Ichter, B. Landry, E. Schmerling, and M. Pavone, “Perception-Aware Motion Planning via Multiobjective Search on GPUs,” in Int. Symp. on Robotics Research, Puerto Varas, Chile, 2017.

    Abstract: In this paper we approach the robust motion planning problem through the lens of perception-aware planning, whereby we seek a low-cost motion plan subject to a separate constraint on perception localization quality. To solve this problem we introduce the Multiobjective Perception-Aware Planning (MPAP) algorithm which explores the state space via a multiobjective search, considering both cost and a perception heuristic. This perception-heuristic formulation allows us to both capture the history dependence of localization drift and represent complex modern perception methods. The solution trajectory from this heuristic-based search is then certified via Monte Carlo methods to be robust. The additional computational burden of perception-aware planning is offset through massive parallelization on a GPU. Through numerical experiments the algorithm is shown to find robust solutions in about a second. Finally, we demonstrate MPAP on a quadrotor flying perception-aware and perception-agnostic plans using Google Tango for localization, finding the quadrotor safely executes the perception-aware plan every time, while crashing over 20% of the time on the perception-agnostic due to loss of localization.

    @inproceedings{IchterLandryEtAl2017,
      author = {Ichter, B. and Landry, B. and Schmerling, E. and Pavone, M.},
      title = {Perception-Aware Motion Planning via Multiobjective Search on {GPUs}},
      booktitle = {{Int. Symp. on Robotics Research}},
      year = {2017},
      address = {Puerto Varas, Chile},
      month = dec,
      url = {https://arxiv.org/pdf/1705.02408.pdf},
      owner = {ichter},
      timestamp = {2018-01-16}
    }