Awards


During the awards ceremony, we will be presenting the conference best paper awards in addition to the International Journal of Robotics Research best paper award.

The best conference papers are selected from a group of finalists by an awards committee. Here is a list of past finalists and winners.

Best Paper Award

This award is given to the best paper of the conference.

Winner:

A Magnetically-Actuated Untethered Jellyfish-Inspired Soft Milliswimmer Untethered small-scale soft robots can potentially be used in healthcare and biomedical applications. They can access small spaces and reshape their bodies in a programmable manner to adapt to unstructured environments and have diverse dynamic behaviors. However, the functionalities of current miniature soft robots are limited, restricting their applications in medical procedures. Taking the advantage of the shape-programmable ability of magnetic soft composite materials, here we propose an untethered soft millirobot (jellyfishbot) that can swim like a jellyfish by time- and trajectory-asymmetric up and down beating of its lappets. Its swimming speed and direction can be controlled by tuning the magnitude, frequency, and direction of the external oscillating magnetic field. We demonstrate that such jellyfishbot can perform several tasks that could be useful towards medical applications, such as delivering drugs, clogging a narrow tube or vessel, and patching a target area under ultrasound imaging-based guiding. The millirobot presented in this paper could be used inside organs filled with fluids completely, such as a bladder or inflated stomach.
[Full Paper]

Ren, Ziyu; Wang, Tianlu; Hu, Wenqi; Sitti, Metin

Finalists:

Robot Packing with Known Items and Nondeterministic Arrival Order This paper formulates two variants of packing problems in which the set of items is known but the arrival order is unknown. The goal is to certify that the items can be packed in a given container, and/or to optimize the size or cost of a container so that that the items are guaranteed to be packable, regardless of arrival order. The Nondeterministically ordered packing (NDOP) variant asks to generate a certificate that a packing plan exists for every ordering of items. Quasi-online packing (QOP) asks to generate a partially-observable packing policy that chooses the item location as each subsequent item is revealed. Theoretical analysis demonstrates that even the simple subproblem of verifying feasibility of a packing policy is NP-complete. Despite this worst-case complexity, practical solvers for both NDOP and QOP are developed, and experiments demonstrate their application to packing irregular 3D shapes with manipulator loading constraints.
[Full Paper]

Wang, Fan; Hauser, Kris

Equivalence of the Projected Forward Dynamics and the Dynamically Consistent Inverse Solution The analysis, design, and motion planning of robotic systems, often relies on its forward and inverse dynamic models. When executing a task involving interaction with the environment, both the task and the environment impose constraints on the robotÂ’s motion. For modeling such systems, we need to incorporate these constraints in the robotÂ’s dynamic model. In this paper, we define the class of Task-based Constraints (TbC) to prove that the forward dynamic models of a constrained system obtained through the Projection-based Dynamics (PbD), and the Operational Space Formulation (OSF) are equivalent. In order to establish such equivalence, we first generalize the OSF to a rank deficient Jacobian. This generalization allow us to numerically handle redundant constraints and singular configurations, without having to use different controllers in the vicinity of such configurations. We then reformulate the PbD constraint inertia matrix, generalizing all its previous distinct algebraic variations. We also analyse the condition number of different constraint inertia matrices, which affects the numerical stability of its inversion. Furthermore, we show that we can recover the operational space control with constraints from a multiple Task-based Constraint abstraction.
[Full Paper]

Moura, Joao; Ivan, Vladimir; Erden, Mustafa Suphi; Vijayakumar, Sethu

Commonsense Reasoning and Knowledge Acquisition to Guide Deep Learning on Robots Algorithms based on deep network models are being used for many pattern recognition and decision-making tasks in robotics and AI. Training these models requires a large labeled dataset and considerable computational resources, which are not readily available in many domains. Also, it is difficult to understand the internal representations and reasoning mechanisms of these models. The architecture described in this paper attempts to address these limitations by drawing inspiration from research in cognitive systems. It uses non-monotonic logical reasoning with incomplete commonsense domain knowledge, and inductive learning of previously unknown constraints on the domain's states, to guide the construction of deep network models based on a small number of relevant training examples. As a motivating example, we consider a robot reasoning about the stability and partial occlusion of configurations of objects in simulated images. Experimental results indicate that in comparison with an architecture based just on deep networks, our architecture improves reliability, and reduces the sample complexity and time complexity of training deep networks.
[Full Paper]

Mota, Tiago; Sridharan, Mohan

Idiothetic Verticality Estimation through Head Stabilization Strategy The knowledge of the gravitational vertical is fundamental for the autonomous control of humanoids and other free-moving robotic systems such as rovers and drones. This article deals with the hypothesis that the so-called `head stabilization strategy' observed in humans and animals facilitates the estimation of the true vertical from inertial sensing only. This problem is difficult because inertial measurements respond to a combination of gravity and fictitious forces that are hard to disentangle. From simulations and experiments, we found that the angular stabilization of a platform bearing inertial sensors enables the application of the separation principle. This principle, which permits one to design estimators and controllers independently from each other, typically applies to linear systems, but rarely to nonlinear systems. We found empirically that, given inertial measurements, the angular regulation of a platform results in a system that is stable and robust and which provides true vertical estimates as a byproduct of the feedback. We conclude that angularly stabilized inertial measurement platforms could liberate robots from ground-based measurements for postural control, locomotion, and other functions, leading to a true idiothetic sensing modality, that is, not based on any external reference but the gravity field.
[Full Paper]

Farkhatdinov, Ildar; Michalska, Hannah; Berthoz, Alain; Hayward, Vincent

Best Student Paper Award sponsored by Springer on behalf of Autonomous Robots

This award is given to the best paper of the conference whose first author is a student.

Winner:

An Online Learning Approach to Model Predictive Control Model predictive control (MPC) is a powerful technique for solving dynamic control tasks. In this paper, we show that there exists a close connection between MPC and online learning, an abstract theoretical framework for analyzing online decision making in the optimization literature. This new perspective provides a foundation for leveraging powerful online learning algorithms to design MPC algorithms. Specifically, we propose a new algorithm based on dynamic mirror descent (DMD), an online learning algorithm that is designed for non-stationary setups. Our algorithm, Dynamic Mirror Descent Model Predictive Control (DMD-MPC), represents a general family of MPC algorithms that includes many existing techniques as special instances. DMD-MPC also provides a fresh perspective on previous heuristics used in MPC and suggests a principled way to design new MPC algorithms. In the experimental section of this paper, we demonstrate the flexibility of DMD-MPC, presenting a set of new MPC algorithms on a simple simulated cartpole and a simulated and real-world aggressive driving task. A video of the real-world experiment can be found at https://youtu.be/vZST3v0_S9w.
[Full Paper]

Wagener, Nolan; Cheng, Ching-an; Sacks, Jacob; Boots, Byron

Best Systems Paper Award in Memory of Seth Teller

This award is given to outstanding systems papers presented at the RSS conference. The awards committee determines each year if a paper of sufficient quality is among the accepted papers and may decide not to give the award. In years when no award is given, the list of finalists will not be disclosed. This award was given for the first time in 2015 (more information).

Winner:

Learning to Throw Arbitrary Objects with Residual Physics We investigate whether a robot arm can learn to pick and throw arbitrary objects into selected boxes quickly and accurately. Throwing has the potential to increase the physical reachability and picking speed of a robot arm. However, precisely throwing arbitrary objects in unstructured settings presents many challenges: from acquiring reliable pre-throw conditions (e.g. initial pose of object in manipulator) to handling varying object-centric properties (e.g. mass distribution, friction, shape) and dynamics (e.g. aerodynamics). In this work, we propose an end-to-end formulation that jointly learns to infer control parameters for grasping and throwing motion primitives from visual observations (images of arbitrary objects in a bin) through trial and error. Within this formulation, we investigate the synergies between grasping and throwing (i.e., learning grasps that enable more accurate throws) and between simulation and deep learning (i.e., using deep networks to predict residuals on top of control parameters predicted by a physics simulator). The resulting system, TossingBot, is able to grasp and throw arbitrary objects into boxes located outside its maximum reach range at 500+ mean picks per hour (600+ grasps per hour with 84% throwing accuracy); and generalizes to new objects and landing locations. Videos are available at http://tossingbot.cs.princeton.edu
[Full Paper]

Zeng, Andy; Song, Shuran; Lee, Johnny; Rodriguez, Alberto; Funkhouser, Thomas A.

Finalists:

An Online Learning Approach to Model Predictive Control Model predictive control (MPC) is a powerful technique for solving dynamic control tasks. In this paper, we show that there exists a close connection between MPC and online learning, an abstract theoretical framework for analyzing online decision making in the optimization literature. This new perspective provides a foundation for leveraging powerful online learning algorithms to design MPC algorithms. Specifically, we propose a new algorithm based on dynamic mirror descent (DMD), an online learning algorithm that is designed for non-stationary setups. Our algorithm, Dynamic Mirror Descent Model Predictive Control (DMD-MPC), represents a general family of MPC algorithms that includes many existing techniques as special instances. DMD-MPC also provides a fresh perspective on previous heuristics used in MPC and suggests a principled way to design new MPC algorithms. In the experimental section of this paper, we demonstrate the flexibility of DMD-MPC, presenting a set of new MPC algorithms on a simple simulated cartpole and a simulated and real-world aggressive driving task. A video of the real-world experiment can be found at https://youtu.be/vZST3v0_S9w.
[Full Paper]

Wagener, Nolan; Cheng, Ching-an; Sacks, Jacob; Boots, Byron

Differentiable Algorithm Networks for Composable Robot Learning This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems. A DAN is composed of neural network modules, each encoding a differentiable robot algorithm and an associated model; and it is trained end-to-end from data. DAN combines the strengths of model-driven modular system design and data-driven end-to-end learning. The algorithms and models act as structural assumptions to reduce the data requirements for learning; end-to-end learning allows the modules to adapt to one another and compensate for imperfect models and algorithms, in order to achieve the best overall system performance. We illustrate the DAN methodology through a case study on a simulated robot system, which learns to navigate in complex 3-D environments with only local visual observations and an image of a partially correct 2-D floor map.
[Full Paper]

Karkus, Peter; Ma, Xiao; Hsu, David; Kaelbling, Leslie; Lee, Wee Sun; Lozano-Perez, Tomas

Modeling and Control of Soft Robots Using the Koopman Operator and Model Predictive Control Controlling soft robots with precision is a challenge due in large part to the difficulty of constructing models that are amenable to model-based control design techniques. Koopman operator theory offers a way to construct explicit linear dynamical models of soft robots and to control them using established model-based linear control methods. This method is data-driven, yet unlike other data-driven models such as neural networks, it yields an explicit control-oriented linear model rather than just a ``black-box'' input-output mapping. This work describes this Koopman-based system identification method and its application to model predictive controller design. A model and MPC controller of a pneumatic soft robot arm is constructed via the method, and its performance is evaluated over several trajectory following tasks in the real-world. On all of the tasks, the Koopman-based MPC controller outperforms a benchmark MPC controller based on a linear state-space model of the same system.
[Full Paper]

Bruder, Daniel; Gillespie, Brent; Remy, C. David; Vasudevan, Ram

Network Offloading Policies for Cloud Robotics: A Learning-Based Approach Today's robotic systems are increasingly turning to computationally expensive models such as deep neural networks (DNNs) for tasks like localization, perception, planning, and object detection. However, resource-constrained robots, like low-power drones, often have insufficient on-board compute resources or power reserves to scalably run the most accurate, state-of-the art neural network compute models. Cloud robotics allows mobile robots the benefit of offloading compute to centralized servers if they are uncertain locally or want to run more accurate, compute-intensive models. However, cloud robotics comes with a key, often understated cost: communicating with the cloud over congested wireless networks may result in latency or loss of data. In fact, sending high data-rate video or LIDAR from multiple robots over congested networks can lead to prohibitive delay for real-time applications, which we measure experimentally. In this paper, we formulate a novel Robot Offloading Problem - how and when should robots offload sensing tasks, especially if they are uncertain, to improve accuracy while minimizing the cost of cloud communication? We formulate offloading as a sequential decision making problem for robots, and propose a solution using deep reinforcement learning. In both simulations and hardware experiments using state-of-the art vision DNNs, our offloading strategy improves vision task performance by between 1.3-2.3x of benchmark offloading strategies, allowing robots the potential to significantly transcend their on-board sensing accuracy but with limited cost of cloud communication.
[Full Paper]

Chinchali, Sandeep; Sharma, Apoorva; Harrison, James; Elhafsi, Amine; Kang, Daniel; Pergament, Evgenya; Cidon, Eyal; Katti, Sachin; Pavone, Marco