Machine learning assesses surgical skill in robotic-assisted surgery.
problem Subjective evaluation of surgeon skill in robotic-assisted surgery.
method Machine learning applied to six movement features for classification.
result Framework classifies surgical skill with 85.7% accuracy.
Simple attention model outperforms complex sEMG classifiers.
problem Improving myoelectric control for robotic prosthetics.
method Attention-based model for sEMG signal classification.
result Simple model achieves benchmark results on multiple datasets.
Gradient surgery improves multi-task learning efficiency.
problem Challenges in sharing structure across multiple tasks.
method Gradient projection onto normal plane of conflicting gradients.
result Substantial gains in efficiency and performance.
A deep learning method automates tensioning for surgical tissue cutting.
problem Automating tensioning for surgical tissue cutting to improve accuracy and reliability.
method Deep reinforcement learning for modeling an autonomous tensioning planner.
result The proposed method outperforms existing methods in terms of performance and robustness.
Z-Net improves 3D CT volume segmentation for surgical planning.
problem Discontinuities and class-imbalances in 3D CT volume segmentation.
method Z-Net uses anisotropic spatial separable convolutions to preserve full field-of-view.
result Z-Net achieves up to 12.6% improvement in IoU for CT segmentation.
The paper studies causal effects of multiple treatments in healthcare databases with rare outcomes.
problem Estimating causal effects of multiple treatments in healthcare databases with rare outcomes.
method The paper designs three sets of simulations and compares the operating characteristics of three types of methods: Bayesian Additive Regression Trees (BART), regression adjustment on multivariate spline of generalized propensity scores (RAMS), and inverse probability of treatment weighting (IPTW) with multinomial logistic regression or generalized boosted models.
result BART and RAMS provide lower bias and mean squared error compared to IPTW methods.
Instantiation-Net reconstructs 3D mesh from single 2D image for right ventricle.
problem Reconstructing 3D shape from limited 2D images for surgical navigation.
method Combines DCNN for feature extraction and GCN for mesh reconstruction.
result Demonstrates practical strength and potential clinical use.
Automates decision-making for human operators managing multiple robots.
problem Limited human operator attention when controlling multiple robots.
method Learned model of user preferences from easy settings to automatically identify the most critical robot.
result Automated decision-making can assist human operators in managing more robots than their attention allows.
Robotics: Rolling robots on a moving platform can be controlled.
problem Controlling the motion of rolling robots atop a moving platform.
method Developed a mathematical model and demonstrated simulations.
result Platform acceleration can control robot's heading and motion.
Robots learn to handle complex tasks creatively using DRL.
problem Robotic manipulation challenges and intelligence.
method Designing challenging manipulation tasks, applying DRL for robot training.
result Robots exhibit creative and non-intuitive problem-solving.
Robot learns sensorimotor relationships through exploration.
problem Autonomous acquisition of sensorimotor contingencies by robots.
method Developmental framework encoding predictive models of sensorimotor experience.
result Robot discovers the environment, objects, and visual field through internal encoding of sensorimotor contingencies.
ROBEL platform accelerates reinforcement learning with low-cost robots.
problem Accelerating reinforcement learning research in robotics.
method Open-source platform of cost-effective robots for real-world reinforcement learning.
result Robots D'Claw and D'Kitty facilitate learning dexterous manipulation and agile locomotion tasks.
Robot learns human interaction skills through multimodal deep reinforcement learning.
problem Developing robots with human-like social interaction skills.
method Multimodal Deep Q-Network (MDQN) for end-to-end reinforcement learning.
result Robot successfully learned basic interaction skills after 14 days of interaction.
New method improves smoothness of robot learning.
problem Jerky motion patterns on real robots from Deep RL exploration.
method Adapting state-dependent exploration to Deep RL algorithms with gSDE.
result Improved exploration and performance on real robots.
Dreaming model enables robots to learn policies from images.
problem Learning to control robots based on images is challenging.
method Learning a future representation/scene regressor conditioned on robot actions.
result Dreaming model enables policy learning that transfers to real-world.
Robots learn to navigate rough terrain using reinforcement learning.
problem Generalizing robot behavior to new, unseen rough terrains.
method PPMC RL Training Algorithm
result Robots achieve 100% success rate in learning new rough terrain maps.
Convolutional neural networks improve surgical skill evaluation.
problem Manual feedback from senior surgeons is laborious, expensive, and subjective.
method Fully convolutional neural networks (CNNs) for classifying and regressing surgical skills.
result Deep neural networks achieve competitive performance on JIGSAWS dataset.
Robots gather information resiliently despite failures and attacks.
problem Resilient information gathering in adversarial or failure-prone environments.
method First scalable algorithm for minimal communication, system-wide resiliency, and provable approximation performance.
result Algorithm ensures optimal or near-optimal solutions for any number of failures and attacks.
A new framework for robot block-stacking tasks using causal probabilistic models.
problem Robots fail outside controlled environments due to uncertainty and lack of explicit design for all scenarios.
method Causal probabilistic framework combining causal models and probabilistic representations of noise.
result Robots can perceive, reason about, and explain their environment for block-stacking tasks.
Robot learns to interact naturally with humans through neural attention.
problem Developing a robot that can respond to complex human behaviors in a natural way.
method Multimodal Deep Attention Recurrent Q-Network trained using reinforcement learning.
result The robot learned to respond to human behaviors in a perceivable and socially acceptable manner.
This research evaluates learning models for bionic robots, focusing on transfer function identification.
problem Developers need guidance on selecting and constructing transfer functions for bionic robots.
method Comprehensive evaluation strategy including data collection, learning model selection, comparative analysis, and transfer function identification.
result A framework for effectively dealing with multi-input multi-output robotic data.
Study improves reliability of reinforcement learning with real-world robots.
problem Difficulty in setting up reinforcement learning tasks with real-world robots.
method Developed a learning task with a UR5 robotic arm to study task setup.
result Learning performance is highly sensitive to task setup details.
Robots learn conservatively from human corrections, avoiding unintended changes to their objectives.
problem Robots learn from human corrections but may not align with the intended objectives due to misspecified hypothesis spaces.
method Robots reason in real-time about the relevance of human corrections to their hypothesis space, learning more conservatively.
result Robots can avoid unintended learning from human corrections, improving alignment with intended objectives.
A new Q-learning controller improves line follower robot control.
problem Challenges in controlling line follower robots due to unknown mechanical characteristics and uncertainties.
method Simulated annealing based Q learning method to address controller performance issues.
result The proposed controller outperforms conventional P controllers in line follower robots.
Neuro-robotics model shows gestures improve counting ability.
problem Improving counting ability in robots and children.
method Introduces a neuro-robotics model trained with pointing data, tests various model and training conditions.
result Model's performance aligns with human children's learning to count.
Paper addresses hypothesis space misspecification in learning from human demonstrations and corrections.
problem Hypothesis space misspecification in learning from human demonstrations and corrections.
method Reason explicitly about how well the robot can explain human inputs given its hypothesis space.
result Demonstrates method on a 7 DOF robot manipulator.
Controller stabilizes spherical robot's position and line-of-sight.
problem Stabilizing a spherical robot's position and line-of-sight.
method Geometric control law with feedforward and proportional-derivative control.
result Controller performance validated through simulations.
Robot learns agile leg movements from simulations to real robots.
problem Training legged robots for dynamic maneuvers is challenging and expensive.
method Simulation-based reinforcement learning for real legged systems.
result ANYmal robot can follow high-level commands, run faster, and recover from falls.
This work tackles force control for contact-rich manipulation tasks with rigid robots using RL.
problem Challenges in working with real robotic hardware, especially position-controlled robots.
method Combines RL with traditional force control techniques, implementing parallel position/force control and admittance control.
result Validated methods on both simulation and real robot (UR3 e-series) for force control.
The paper learns robot skills from demonstrations without supervision.
problem Discovering robotic options from unlabelled demonstrations.
method Temporal variational inference for latent variable learning.
result The framework can learn options across multiple datasets.
Robot learns tool use from effects, detecting features of tools, objects, and actions.
problem Teaching robots to understand and manipulate objects using tools.
method Deep learning model trained on sensory-motor data from a robot performing a tool-use task.
result Robot can detect features of tools, objects, and actions from effects of object manipulation.
Study aims to develop a humanoid robot dialogue system.
problem Current dialogue systems lack attention to non-verbal cues.
method Participated in a competition to develop a system with facial expressions and gaze control.
result Developed a humanoid robot dialogue system.
Top 8 robotic vision systems tackled lifelong object recognition challenges.
problem Lifelong learning in robotic vision for varied, dynamic environments.
method Design of a dataset with diverse conditions and rules for evaluation.
result Robotic vision systems improved over time with dynamic object appearances.
Robot learns to dress people safely using deep learning.
problem Robots lack common sense about physical interactions with clothing.
method Deep recurrent model predicts garment forces, used with MPC.
result Robot can better assist in dressing without high forces.
Paper proposes a method to control robots of different shapes efficiently.
problem Learning optimal control policies for robots of various shapes is challenging.
method Hierarchical architecture with hypernetworks and fixed attention mechanism.
result Method improves learning performance and generalizes to unseen morphologies.
Robot solves Rubik's cube using simulation-trained models.
problem Solving Rubik's cube with a robot hand.
method Automatic domain randomization (ADR) and a custom robot platform.
result Control policies and vision state estimators trained with ADR exhibit improved sim2real transfer.
Robot learns new tasks sequentially without forgetting past ones.
problem Teaching a robot to solve tasks in a continual learning scenario.
method Policy distillation and sim2real transfer.
result Robot can solve all encountered tasks without forgetting past ones.
A new optimizer d-AmsGrad improves deep learning for robot learning in non-stationary problems.
problem Noise and outliers in real-world data make deep learning challenging for robot learning.
method Proposed an improved version of AmsGrad optimizer that slowly decays the maximum second momentum to adapt to non-stationary problems.
result The new optimizer outperformed baseline optimizers in robotics problems.
Robot learns from human demonstrations to work autonomously.
problem Leveraging human and robotic strengths in human-robot systems.
method Bayesian inference for detecting and classifying human heterogeneity.
result Bayesian approach outperforms conventional methods by up to 12.8. The paper formalizes robot environments using topological concepts.
problem Determining indistinguishable environments for robots.
method Formalizing robot systems as topological dynamical systems, using covering maps and bisimulation.
result Covering maps can witness indistinguishability of environments under general conditions.
Robot learns from human actions to perform complex tasks.
problem Learning complex skills from interaction data with embodiment differences.
method Formulated graphical model, treated action as observed variable, used domain-dependent prior.
result Robotic planning agent can learn tool use from human observations.
This thesis tackles learning reward functions from human comparative feedback.
problem Designing reward functions for complex tasks is challenging and humans often provide suboptimal demonstrations.
method Proposes learning reward functions from comparative feedback (pairwise comparisons, best-of-many choices, rankings, scaled comparisons) and active learning techniques.
result Demonstrates the effectiveness of learning reward functions from comparative feedback in various domains.
Robot learns to imitate human interactions through deep learning.
problem Teaching robots to coordinate actions with human partners.
method Deep learning framework for motion embedding, prediction, and trajectory generation.
result Importance of predictive and adaptive components for successful imitation.
A conceptor-based approach helps robots recognize human internal states.
problem Recognizing human internal states for diagnostic purposes in autism therapy.
method A conceptor-based classifier to classify internal states.
result Initial results show potential for detailed diagnostic information.
Diffusion models enhance robotic manipulation through probabilistic multi-modal learning.
problem Enhancing robotic manipulation through robust and multi-modal learning.
method Probabilistic diffusion models integrating imitation and reinforcement learning.
result Diffusion models improve grasp learning, trajectory planning, and data augmentation in robotics.
Paper introduces a new web robot traffic generator.
problem Lack of realistic web robot traffic generators.
method Statistical and Bayesian models fitted to robot traffic data.
result Generated traffic mimics real robot traffic characteristics.
RL controls small soccer robots in a real league, beating human-designed policies.
problem Training robots to play complex, real-world sports.
method Sim-to-Real RL approach, training in simulated environment, applying to real-world robots.
result Robots learned policies to compete effectively, beating human-designed strategies.
A robot assists a human in a bandit task to learn and improve performance.
problem Learning preferences in humans when they are also learning.
method Introduces assistive multi-armed bandit, where a robot helps a human maximize cumulative reward.
result Human performance can be better when effectively communicating observed rewards to the robot, not just by learning optimally.