121 research outputs found
Safe Interactive Industrial Robots using Jerk-based Safe Set Algorithm
The need to increase the flexibility of production lines is calling for
robots to collaborate with human workers. However, existing interactive
industrial robots only guarantee intrinsic safety (reduce collision impact),
but not interactive safety (collision avoidance), which greatly limited their
flexibility. The issue arises from two limitations in existing control software
for industrial robots: 1) lack of support for real-time trajectory
modification; 2) lack of intelligent safe control algorithms with guaranteed
collision avoidance under robot dynamics constraints. To address the first
issue, a jerk-bounded position controller (JPC) was developed previously. This
paper addresses the second limitation, on top of the JPC. Specifically, we
introduce a jerk-based safe set algorithm (JSSA) to ensure collision avoidance
while considering the robot dynamics constraints. The JSSA greatly extends the
scope of the original safe set algorithm, which has only been applied for
second-order systems with unbounded accelerations. The JSSA is implemented on
the FANUC LR Mate 200id/7L robot and validated with HRI tasks. Experiments show
that the JSSA can consistently keep the robot at a safe distance from the human
while executing the designated task
A Lightweight and Transferable Design for Robust LEGO Manipulation
LEGO is a well-known platform for prototyping pixelized objects. However,
robotic LEGO prototyping (i.e. manipulating LEGO bricks) is challenging due to
the tight connections and accuracy requirement. This paper investigates safe
and efficient robotic LEGO manipulation. In particular, this paper reduces the
complexity of the manipulation by hardware-software co-design. An end-of-arm
tool (EOAT) is designed, which reduces the problem dimension and allows large
industrial robots to easily manipulate LEGO bricks. In addition, this paper
uses evolution strategy to safely optimize the robot motion for LEGO
manipulation. Experiments demonstrate that the EOAT performs reliably in
manipulating LEGO bricks and the learning framework can effectively and safely
improve the manipulation performance to a 100\% success rate. The co-design is
deployed to multiple robots (i.e. FANUC LR-mate 200id/7L and Yaskawa GP4) to
demonstrate its generalizability and transferability. In the end, we show that
the proposed solution enables sustainable robotic LEGO prototyping, in which
the robot can repeatedly assemble and disassemble different prototypes
Partial Identification and Inference in Censored Quantile Regression: A Sensitivity Analysis
In this paper we characterize the identified set and construct asymptotically valid and non-conservative confidence sets for the quantile regression coeffi cient in a linear quantile regression model, where the dependent variable is subject to possibly dependent censoring. The underlying censoring mechanism is characterized by an Archimedean copula for the dependent variable and the censoring variable. For a broad class of Archimedean copulas, we characterize an outer set of the corresponding identified set for the quantile regression coeffi cient via inequality constraints. For one-parameter ordered families of Archimedean copulas, we construct simple confidence sets by inverting asymptotically pivotal statistics related to kernel-based model specification testing. The methodology we develop in this paper allows practitioners to conduct sensitivity analysis of the robustness of conclusions on the quantile regression coeffi cient to the independent censoring mechanism. Bootstrap confidence sets are also constructed. Interpreting the dependent variable and the censoring variable in our censored quantile regression model as two competing risks, our methodology is useful in duration analysis with possibly dependent competin
Task-Agnostic Adaptation for Safe Human-Robot Handover
Human-robot interaction (HRI) is an important component to improve the
flexibility of modern production lines. However, in real-world applications,
the task (\ie the conditions that the robot needs to operate on, such as the
environmental lighting condition, the human subjects to interact with, and the
hardware platforms) may vary and it remains challenging to optimally and
efficiently configure and adapt the robotic system under these changing tasks.
To address the challenge, this paper proposes a task-agnostic adaptable
controller that can 1) adapt to different lighting conditions, 2) adapt to
individual behaviors and ensure safety when interacting with different humans,
and 3) enable easy transfer across robot platforms with different control
interfaces. The proposed framework is tested on a human-robot handover task
using the FANUC LR Mate 200id/7L robot and the Kinova Gen3 robot. Experiments
show that the proposed task-agnostic controller can achieve consistent
performance across different tasks
Proactive Human-Robot Co-Assembly: Leveraging Human Intention Prediction and Robust Safe Control
Human-robot collaboration (HRC) is one key component to achieving flexible
manufacturing to meet the different needs of customers. However, it is
difficult to build intelligent robots that can proactively assist humans in a
safe and efficient way due to several challenges. First, it is challenging to
achieve efficient collaboration due to diverse human behaviors and data
scarcity. Second, it is difficult to ensure interactive safety due to
uncertainty in human behaviors. This paper presents an integrated framework for
proactive HRC. A robust intention prediction module, which leverages prior task
information and human-in-the-loop training, is learned to guide the robot for
efficient collaboration. The proposed framework also uses robust safe control
to ensure interactive safety under uncertainty. The developed framework is
applied to a co-assembly task using a Kinova Gen3 robot. The experiment
demonstrates that our solution is robust to environmental changes as well as
different human preferences and behaviors. In addition, it improves task
efficiency by approximately 15-20%. Moreover, the experiment demonstrates that
our solution can guarantee interactive safety during proactive collaboration.Comment: 7th IEEE Conference on Control Technology and Applications (CCTA
2023
Simulation-aided Learning from Demonstration for Robotic LEGO Construction
Recent advancements in manufacturing have a growing demand for fast,
automatic prototyping (i.e. assembly and disassembly) capabilities to meet
users' needs. This paper studies automatic rapid LEGO prototyping, which is
devoted to constructing target LEGO objects that satisfy individual
customization needs and allow users to freely construct their novel designs. A
construction plan is needed in order to automatically construct the
user-specified LEGO design. However, a freely designed LEGO object might not
have an existing construction plan, and generating such a LEGO construction
plan requires a non-trivial effort since it requires accounting for numerous
constraints (e.g. object shape, colors, stability, etc.). In addition,
programming the prototyping skill for the robot requires the users to have
expert programming skills, which makes the task beyond the reach of the general
public. To address the challenges, this paper presents a simulation-aided
learning from demonstration (SaLfD) framework for easily deploying LEGO
prototyping capability to robots. In particular, the user demonstrates
constructing the customized novel LEGO object. The robot extracts the task
information by observing the human operation and generates the construction
plan. A simulation is developed to verify the correctness of the learned
construction plan and the resulting LEGO prototype. The proposed system is
deployed to a FANUC LR-mate 200id/7L robot. Experiments demonstrate that the
proposed SaLfD framework can effectively correct and learn the prototyping
(i.e. assembly and disassembly) tasks from human demonstrations. And the
learned prototyping tasks are realized by the FANUC robot
Robotic Planning under Hierarchical Temporal Logic Specifications
Past research into robotic planning with temporal logic specifications,
notably Linear Temporal Logic (LTL), was largely based on singular formulas for
individual or groups of robots. But with increasing task complexity, LTL
formulas unavoidably grow lengthy, complicating interpretation and
specification generation, and straining the computational capacities of the
planners. In order to maximize the potential of LTL specifications, we
capitalized on the intrinsic structure of tasks and introduced a hierarchical
structure to LTL specifications. In contrast to the "flat" structure, our
hierarchical model has multiple levels of compositional specifications and
offers benefits such as greater syntactic brevity, improved interpretability,
and more efficient planning. To address tasks under this hierarchical temporal
logic structure, we formulated a decomposition-based method. Each specification
is first broken down into a range of temporally interrelated sub-tasks. We
further mine the temporal relations among the sub-tasks of different
specifications within the hierarchy. Subsequently, a Mixed Integer Linear
Program is utilized to generate a spatio-temporal plan for each robot. Our
hierarchical LTL specifications were experimentally applied to domains of
robotic navigation and manipulation. Results from extensive simulation studies
illustrated both the enhanced expressive potential of the hierarchical form and
the efficacy of the proposed method.Comment: 8 pages, 4 figure
On the accuracy and efficiency of group-wise clipping in differentially private optimization
Recent advances have substantially improved the accuracy, memory cost, and
training speed of differentially private (DP) deep learning, especially on
large vision and language models with millions to billions of parameters. In
this work, we thoroughly study the per-sample gradient clipping style, a key
component in DP optimization. We show that different clipping styles have the
same time complexity but instantiate an accuracy-memory trade-off: while the
all-layer clipping (of coarse granularity) is the most prevalent and usually
gives the best accuracy, it incurs heavier memory cost compared to other
group-wise clipping, such as the layer-wise clipping (of finer granularity). We
formalize this trade-off through our convergence theory and complexity
analysis. Importantly, we demonstrate that the accuracy gap between group-wise
clipping and all-layer clipping becomes smaller for larger models, while the
memory advantage of the group-wise clipping remains. Consequently, the
group-wise clipping allows DP optimization of large models to achieve high
accuracy and low peak memory simultaneously
Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation
News recommendation is critical for personalized news access. Most existing
news recommendation methods rely on centralized storage of users' historical
news click behavior data, which may lead to privacy concerns and hazards.
Federated Learning is a privacy-preserving framework for multiple clients to
collaboratively train models without sharing their private data. However, the
computation and communication cost of directly learning many existing news
recommendation models in a federated way are unacceptable for user clients. In
this paper, we propose an efficient federated learning framework for
privacy-preserving news recommendation. Instead of training and communicating
the whole model, we decompose the news recommendation model into a large news
model maintained in the server and a light-weight user model shared on both
server and clients, where news representations and user model are communicated
between server and clients. More specifically, the clients request the user
model and news representations from the server, and send their locally computed
gradients to the server for aggregation. The server updates its global user
model with the aggregated gradients, and further updates its news model to
infer updated news representations. Since the local gradients may contain
private information, we propose a secure aggregation method to aggregate
gradients in a privacy-preserving way. Experiments on two real-world datasets
show that our method can reduce the computation and communication cost on
clients while keep promising model performance
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