arXiv subjects

Dinesh Manocha

Publications and source records attributed to Dinesh Manocha.

At least 433 records · Page 24Linked to original sources

arXiv2018

Fast Motion Planning for High-DOF Robot Systems Using Hierarchical System Identification

We present an efficient algorithm for motion planning and control of a robot system with a high number of degrees-of-freedom. These include high-DOF soft robots or an articulated robot interacting with a deformable environment. Our approach takes into account dynamics constraints and present a novel technique to accelerate the forward dynamic computation using a data-driven method. We precompute the forward dynamic function of the robot system on a hierarchical adaptive grid. Furthermore, we exploit the properties of underactuated robot systems and perform these computations for a few DOFs. We provide error bounds for our approximate forward dynamics computation and use our approach for optimization-based motion planning and reinforcement-learning-based feedback control. Our formulation is used for motion planning of two high DOF robot systems: a high-DOF line-actuated elastic robot arm and an underwater swimming robot operating in water. As compared to prior techniques based on exact dynamic function computation, we observe one to two orders of magnitude improvement in performance.

cs.RO↗

arXiv2018

Getting Robots Unfrozen and Unlost in Dense Pedestrian Crowds

We aim to enable a mobile robot to navigate through environments with dense crowds, e.g., shopping malls, canteens, train stations, or airport terminals. In these challenging environments, existing approaches suffer from two common problems: the robot may get frozen and cannot make any progress toward its goal, or it may get lost due to severe occlusions inside a crowd. Here we propose a navigation framework that handles the robot freezing and the navigation lost problems simultaneously. First, we enhance the robot's mobility and unfreeze the robot in the crowd using a reinforcement learning based local navigation policy developed in our previous work~\cite{long2017towards}, which naturally takes into account the coordination between the robot and the human. Secondly, the robot takes advantage of its excellent local mobility to recover from its localization failure. In particular, it dynamically chooses to approach a set of recovery positions with rich features. To the best of our knowledge, our method is the first approach that simultaneously solves the freezing problem and the navigation lost problem in dense crowds. We evaluate our method in both simulated and real-world environments and demonstrate that it outperforms the state-of-the-art approaches. Videos are available at https://sites.google.com/view/rlslam.

cs.RO↗

arXiv2018

Data-Driven Modeling of Group Entitativity in Virtual Environments

We present a data-driven algorithm to model and predict the socio-emotional impact of groups on observers. Psychological research finds that highly entitative i.e. cohesive and uniform groups induce threat and unease in observers. Our algorithm models realistic trajectory-level behaviors to classify and map the motion-based entitativity of crowds. This mapping is based on a statistical scheme that dynamically learns pedestrian behavior and computes the resultant entitativity induced emotion through group motion characteristics. We also present a novel interactive multi-agent simulation algorithm to model entitative groups and conduct a VR user study to validate the socio-emotional predictive power of our algorithm. We further show that model-generated high-entitativity groups do induce more negative emotions than low-entitative groups.

cs.GR↗

arXiv2018

Interactive Surveillance Technologies for Dense Crowds

We present an algorithm for realtime anomaly detection in low to medium density crowd videos using trajectory-level behavior learning. Our formulation combines online tracking algorithms from computer vision, non-linear pedestrian motion models from crowd simulation, and Bayesian learning techniques to automatically compute the trajectory-level pedestrian behaviors for each agent in the video. These learned behaviors are used to segment the trajectories and motions of different pedestrians or agents and detect anomalies. We demonstrate the interactive performance on the PETS ARENA dataset as well as indoor and outdoor crowd video benchmarks consisting of tens of human agents. We also discuss the implications of recent public policy and law enforcement issues relating to surveillance and our research.

cs.CV↗

arXiv2018

Diffraction-Aware Sound Localization for a Non-Line-of-Sight Source

We present a novel sound localization algorithm for a non-line-of-sight (NLOS) sound source in indoor environments. Our approach exploits the diffraction properties of sound waves as they bend around a barrier or an obstacle in the scene. We combine a ray tracing based sound propagation algorithm with a Uniform Theory of Diffraction (UTD) model, which simulate bending effects by placing a virtual sound source on a wedge in the environment. We precompute the wedges of a reconstructed mesh of an indoor scene and use them to generate diffraction acoustic rays to localize the 3D position of the source. Our method identifies the convergence region of those generated acoustic rays as the estimated source position based on a particle filter. We have evaluated our algorithm in multiple scenarios consisting of a static and dynamic NLOS sound source. In our tested cases, our approach can localize a source position with an average accuracy error, 0.7m, measured by the L2 distance between estimated and actual source locations in a 7m*7m*3m room. Furthermore, we observe 37% to 130% improvement in accuracy over a state-of-the-art localization method that does not model diffraction effects, especially when a sound source is not visible to the robot.

cs.RO↗

arXiv2018

Safe Navigation with Human Instructions in Complex Scenes

In this paper, we present a robotic navigation algorithm with natural language interfaces, which enables a robot to safely walk through a changing environment with moving persons by following human instructions such as "go to the restaurant and keep away from people". We first classify human instructions into three types: the goal, the constraints, and uninformative phrases. Next, we provide grounding for the extracted goal and constraint items in a dynamic manner along with the navigation process, to deal with the target objects that are too far away for sensor observation and the appearance of moving obstacles like humans. In particular, for a goal phrase (e.g., "go to the restaurant"), we ground it to a location in a predefined semantic map and treat it as a goal for a global motion planner, which plans a collision-free path in the workspace for the robot to follow. For a constraint phrase (e.g., "keep away from people"), we dynamically add the corresponding constraint into a local planner by adjusting the values of a local costmap according to the results returned by the object detection module. The updated costmap is then used to compute a local collision avoidance control for the safe navigation of the robot. By combining natural language processing, motion planning, and computer vision, our developed system is demonstrated to be able to successfully follow natural language navigation instructions to achieve navigation tasks in both simulated and real-world scenarios. Videos are available at https://sites.google.com/view/snhi

cs.RO↗

arXiv2018

CrowdMove: Autonomous Mapless Navigation in Crowded Scenarios

Navigation is an essential capability for mobile robots. In this paper, we propose a generalized yet effective 3M (i.e., multi-robot, multi-scenario, and multi-stage) training framework. We optimize a mapless navigation policy with a robust policy gradient algorithm. Our method enables different types of mobile platforms to navigate safely in complex and highly dynamic environments, such as pedestrian crowds. To demonstrate the superiority of our method, we test our methods with four kinds of mobile platforms in four scenarios. Videos are available at https://sites.google.com/view/crowdmove.

cs.RO↗

arXiv2018

Time Integrating Articulated Body Dynamics Using Position-Based Collocation Method

We present a new time integrator for articulated body dynamics. We formulate the governing equations of the dynamics using only the position variables and recast the position-based articulated dynamics as an optimization problem. Our reformulation allows us to integrate the dynamics in a fully implicit manner without computing high-order derivatives. Therefore, under arbitrarily large timestep sizes, the stability of our time integration scheme is guaranteed using an off-the-shelf numerical optimizer. In addition to stability, we show that, similar to the Runge-Kutta method, the accuracy of our time integrator can also be increased arbitrarily by using a high-order collocation method. We provide efficient algorithms to perform time integration using our position-based formulation. We show that each iteration of optimization has a complexity of O(N) using Quasi-Newton method or O(N^2) using Newton's method, where N is the number of links. Finally, our method is highly parallelizable and can be accelerated using a Graphics Processing Unit (GPU). We highlight the efficiency and stability of our method on different benchmarks and compare the performance with prior articulated body dynamics simulation methods based on the Newton-Euler's equation. Our method is stable under a timestep size as large as 0.1s. Using a larger timestep size and less timesteps, our method achieves up to 4 times speedup on a single-core CPU. With GPU acceleration, we observe an additional 3-6 times speedup over a 4-core CPU.

cs.RO↗

arXiv2018

The Socially Invisible Robot: Navigation in the Social World using Robot Entitativity

We present a real-time, data-driven algorithm to enhance the social-invisibility of robots within crowds. Our approach is based on prior psychological research, which reveals that people notice and--importantly--react negatively to groups of social actors when they have high entitativity, moving in a tight group with similar appearances and trajectories. In order to evaluate that behavior, we performed a user study to develop navigational algorithms that minimize entitativity. This study establishes a mapping between emotional reactions and multi-robot trajectories and appearances and further generalizes the finding across various environmental conditions. We demonstrate the applicability of our entitativity modeling for trajectory computation for active surveillance and dynamic intervention in simulated robot-human interaction scenarios. Our approach empirically shows that various levels of entitative robots can be used to both avoid and influence pedestrians while not eliciting strong emotional reactions, giving multi-robot systems socially-invisibility.

cs.RO↗

arXiv2018

PORCA: Modeling and Planning for Autonomous Driving among Many Pedestrians

This paper presents a planning system for autonomous driving among many pedestrians. A key ingredient of our approach is PORCA, a pedestrian motion prediction model that accounts for both a pedestrian's global navigation intention and local interactions with the vehicle and other pedestrians. Unfortunately, the autonomous vehicle does not know the pedestrian's intention a priori and requires a planning algorithm that hedges against the uncertainty in pedestrian intentions. Our planning system combines a POMDP algorithm with the pedestrian motion model and runs in near real time. Experiments show that it enables a robot vehicle to drive safely, efficiently, and smoothly among a crowd with a density of nearly one person per square meter.

cs.RO↗

arXiv2018

Learning-based Feedback Controller for Deformable Object Manipulation

In this paper, we present a general learning-based framework to automatically visual-servo control the position and shape of a deformable object with unknown deformation parameters. The servo-control is accomplished by learning a feedback controller that determines the robotic end-effector's movement according to the deformable object's current status. This status encodes the object's deformation behavior by using a set of observed visual features, which are either manually designed or automatically extracted from the robot's sensor stream. A feedback control policy is then optimized to push the object toward a desired featured status efficiently. The feedback policy can be learned either online or offline. Our online policy learning is based on the Gaussian Process Regression (GPR), which can achieve fast and accurate manipulation and is robust to small perturbations. An offline imitation learning framework is also proposed to achieve a control policy that is robust to large perturbations in the human-robot interaction. We validate the performance of our controller on a set of deformable object manipulation tasks and demonstrate that our method can achieve effective and accurate servo-control for general deformable objects with a wide variety of goal settings.

cs.RO↗

arXiv2018

Spoke-Darts for High-Dimensional Blue-Noise Sampling

Blue noise sampling has proved useful for many graphics applications, but remains underexplored in high-dimensional spaces due to the difficulty of generating distributions and proving properties about them. We present a blue noise sampling method with good quality and performance across different dimensions. The method, spoke-dart sampling, shoots rays from prior samples and selects samples from these rays. It combines the advantages of two major high-dimensional sampling methods: the locality of advancing front with the dimensionality-reduction of hyperplanes, specifically line sampling. We prove that the output sampling is saturated with high probability, with bounds on distances between pairs of samples and between any domain point and its nearest sample. We demonstrate spoke-dart applications for approximate Delaunay graph construction, global optimization, and robotic motion planning. Both the blue-noise quality of the output distribution and the adaptability of the intermediate processes of our method are useful in these applications.

cs.GR↗

arXiv2018

Efficient Reciprocal Collision Avoidance between Heterogeneous Agents Using CTMAT

We present a novel algorithm for reciprocal collision avoidance between heterogeneous agents of different shapes and sizes. We present a novel CTMAT representation based on medial axis transform to compute a tight fitting bounding shape for each agent. Each CTMAT is represented using tuples, which are composed of circular arcs and line segments. Based on the reciprocal velocity obstacle formulation, we reduce the problem to solving a low-dimensional linear programming between each pair of tuples belonging to adjacent agents. We precompute the Minkowski Sums of tuples to accelerate the runtime performance. Finally, we provide an efficient method to update the orientation of each agent in a local manner. We have implemented the algorithm and highlight its performance on benchmarks corresponding to road traffic scenarios and different vehicles. The overall runtime performance is comparable to prior multi-agent collision avoidance algorithms that use circular or elliptical agents. Our approach is less conservative and results in fewer false collisions.

cs.AI↗

arXiv2018

Identifying Driver Behaviors using Trajectory Features for Vehicle Navigation

We present a novel approach to automatically identify driver behaviors from vehicle trajectories and use them for safe navigation of autonomous vehicles. We propose a novel set of features that can be easily extracted from car trajectories. We derive a data-driven mapping between these features and six driver behaviors using an elaborate web-based user study. We also compute a summarized score indicating a level of awareness that is needed while driving next to other vehicles. We also incorporate our algorithm into a vehicle navigation simulation system and demonstrate its benefits in terms of safer real-time navigation, while driving next to aggressive or dangerous drivers.

cs.RO↗

arXiv2018

Interactive Sound Rendering on Mobile Devices using Ray-Parameterized Reverberation Filters

We present a new sound rendering pipeline that is able to generate plausible sound propagation effects for interactive dynamic scenes. Our approach combines ray-tracing-based sound propagation with reverberation filters using robust automatic reverb parameter estimation that is driven by impulse responses computed at a low sampling rate.We propose a unified spherical harmonic representation of directional sound in both the propagation and auralization modules and use this formulation to perform a constant number of convolution operations for any number of sound sources while rendering spatial audio. In comparison to previous geometric acoustic methods, we achieve a speedup of over an order of magnitude while delivering similar audio to high-quality convolution rendering algorithms. As a result, our approach is the first capable of rendering plausible dynamic sound propagation effects on commodity smartphones.

cs.GR↗

arXiv2017

I-Planner: Intention-Aware Motion Planning Using Learning Based Human Motion Prediction

We present a motion planning algorithm to compute collision-free and smooth trajectories for high-DOF robots interacting with humans in a shared workspace. Our approach uses offline learning of human actions along with temporal coherence to predict the human actions. Our intention-aware online planning algorithm uses the learned database to compute a reliable trajectory based on the predicted actions. We represent the predicted human motion using a Gaussian distribution and compute tight upper bounds on collision probabilities for safe motion planning. We also describe novel techniques to account for noise in human motion prediction. We highlight the performance of our planning algorithm in complex simulated scenarios and real world benchmarks with 7-DOF robot arms operating in a workspace with a human performing complex tasks. We demonstrate the benefits of our intention-aware planner in terms of computing safe trajectories in such uncertain environments.

cs.RO↗

arXiv2017

Reflection-Aware Sound Source Localization

We present a novel, reflection-aware method for 3D sound localization in indoor environments. Unlike prior approaches, which are mainly based on continuous sound signals from a stationary source, our formulation is designed to localize the position instantaneously from signals within a single frame. We consider direct sound and indirect sound signals that reach the microphones after reflecting off surfaces such as ceilings or walls. We then generate and trace direct and reflected acoustic paths using inverse acoustic ray tracing and utilize these paths with Monte Carlo localization to estimate a 3D sound source position. We have implemented our method on a robot with a cube-shaped microphone array and tested it against different settings with continuous and intermittent sound signals with a stationary or a mobile source. Across different settings, our approach can localize the sound with an average distance error of 0.8m tested in a room of 7m by 7m area with 3m height, including a mobile and non-line-of-sight sound source. We also reveal that the modeling of indirect rays increases the localization accuracy by 40% compared to only using direct acoustic rays.

cs.SD↗

arXiv2017

MixedPeds: Pedestrian Detection in Unannotated Videos using Synthetically Generated Human-agents for Training

We present a new method for training pedestrian detectors on an unannotated set of images. We produce a mixed reality dataset that is composed of real-world background images and synthetically generated static human-agents. Our approach is general, robust, and makes no other assumptions about the unannotated dataset regarding the number or location of pedestrians. We automatically extract from the dataset: i) the vanishing point to calibrate the virtual camera, and ii) the pedestrians' scales to generate a Spawn Probability Map, which is a novel concept that guides our algorithm to place the pedestrians at appropriate locations. After putting synthetic human-agents in the unannotated images, we use these augmented images to train a Pedestrian Detector, with the annotations generated along with the synthetic agents. We conducted our experiments using Faster R-CNN by comparing the detection results on the unannotated dataset performed by the detector trained using our approach and detectors trained with other manually labeled datasets. We showed that our approach improves the average precision by 5-13% over these detectors.

cs.CV↗