Proactive slip control by learned slip model and trajectory adaptation
Authors: Kiyanoush Nazari
Willow Mandil
Amir Masoud Ghalamzan Esfahani
Venue: CoRL 2022 Poster (2022)
Keywords: Manipulation
Motion planning
Slip control
Motion control

Abstract: This paper presents a novel control approach to dealing with a slip during robotic manipulative movements. Slip is a major cause of failure in many robotic grasping and manipulation tasks. Existing works use increased gripping forces to avoid/control slip. However, this may not be feasible, e.g., because (i) the robot cannot increase the gripping force-- the max gripping force has already applied or (ii) an increased force yields a damaged grasped object, such as soft fruit. Moreover, the robot fixes the gripping force when it forms a stable grasp on the surface of an object, and changing the gripping force during manipulative movements in real-time may not be feasible, e.g., with the Franka robot. Hence, controlling the slip by changing gripping forces is not an effective control policy in many settings. We propose a novel control approach to slip avoidance including a learned action-conditioned slip predictor and a constrained optimizer avoiding an expected slip given the desired robot actions. We show the effectiveness of this receding horizon controller in a series of test cases in real robot experimentation. Our experimental results show our proposed data-driven predictive controller can control slip for objects unseen in training. 


Proactive slip control
by learned slip model and trajectory adaptation
Kiyanoush Nazari
Willow Mandil
School of Computer Science University of Lincoln United Kingdom 197496848students. lincoln. ac, uk
School of Computer Science
University of Lincoln United Kingdom 187103700studente lincolnn..... ac uk
Amir Ghalamzan E.
Lincoln Institute for Agri-Food Technology University of Lincoln United Kingdom aghal amzanes faani @lincoln, ac
Abstract: This paper presents novel control approach to dealing with object slip during robotic manipulative movements. Slip is major cause of failure in many robotic grasping and manipulation tasks. Existing works increase grip force avoid/control slip. However, this may not be feasible when (i) the robot cannot increase the gripping force- the max gripping force already applied or (ii) in- creased force damages the grasped object, such as soft fruit. Moreover, the robot fixes the gripping force when it forms a stable grasp on the surface of an object, and changing the gripping force during real-time manipulation may not be an ef- fective control policy. We propose a novel control approach slip avoidance including learned action-conditioned slip predictor and constrained optimiser avoiding a predicted slip given a desired robot action. We show the effectiveness of the proposed trajectory adaptation method with the receding horizon controller with a series of real-robot test cases. Our experimental results show our proposed data-drive predictive controller can control slip for objects unseen in training. (Video and Github)
Keywords: Slip avoidance, Robot Control, Manipulation
Introduction
Sense of touch in humans and primates is key to building sophisticated dexterous manipulation skills [1]. It provides localised contact information on human skin in particular as an effective per- ception in our daily lives [2]. A slip event occurs when the ratio of applied shear to the normal force on the contact area exceeds the static friction coefficient. Recent studies show that human reflexes to the feeling of slip (we use the term slip to refer to the slipping action of an object between fingers) during manipulative movements not only include grip adjustment but also specific movements of the shoulder and elbow [3]. Existing slip avoidance control methods in robotic manipulation using artificial tactile sensing are limited to: (i) grip force adaptation in case of slip [4, 5]. (ii) reactive algorithms that activate after slip onset and detection [6, 7], and (iii) gentle manipulation tasks, e.g., slow lifting movements for 10-20 [cm] [5, 6]. However, increased grip force may not be an effective action when (i) the gripper has already reached its force limits, (ii) the grasped object is delicate/fragile such soft foods (i.e. strawberry where the average grip force damage the fruit is 1.5 N [8]). Moreover, many grippers, such as Robotiq's 's Hand-E Gripper or Franka Emika Gripper, do not have enough force resolution, (iii) the gripper cannot be controlled in real-time due to connection limitations i.e. blocking TCP/IP connections in the Franka Emika gripper.
We propose two novel means of controlling slip by adapting the trajectory of manipulative move- ments: (i) A Reactive Slip Control (RSC) policy, which uses detected slip slip classifier at time t- to reduce the robot velocity to avoid slip; (ii) A Proactive Slip Control (PSC) policy for avoiding slip
6th Conference on Robot Learning (CoRI. 2022), Auckland, New Zealand.

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during robotic manipulative movements (see schematic of the approach in Fig. 1). PSC includes an oetion-conditionel slip predictor, slip classifier at + where h the prediction horizon using an L.TT (Long-Short Term Memory) based neural network and a predictive controller. The controller minimises the likelihood of predicted slip in a prediction horizon by adjusting the robot's planned movements and deviating from the reference trajectories. We use quadratic term penalising devi- ation from a reference trajectory; hence, PSC avoids slip while closely following a given reference trajectory. Our constrained optimisation enables the robot to satisfy its constraints in generating optimal future robot actions. We study the effectiveness of our on-the-fly adaptation of real robot's pre- planned trajectory (which may be minimum time minimum jerk) in series of real robot ex- periments. We compare RSC and PSC in terms of their performance in slip avoidance, computation complexity, and optimality. Our experimental results indicate PSC outperforms RSC and can change the game in robotic manipulation success where increased gripping force is not possible.
2 Related works
Slip detection algorithms are thoroughly reviewed in [9, 10]. [11] Support Vector Machine (SVM) and Random Forest (RF) on SynTouch BioTac's raw tactile readings and [12] use SVM on TacTip signals for slip classification. SVM and RF have computational complexity problems in training for large data sets with high feature dimensionality. Specifying a threshold on the rate of shear forces for slip detection is a common approach for slip classification [13, 4]. However, it needs initial friction estimation, and finding the threshold for large object sets can be expensive. Other techniques rely on analysing
in the incipient slip phase such as spectral analy- sis [14, 15]. These approaches can fail when robot vibrations interfere with sensor-object vibrations. Multi-modal approaches are also proposed to use proximity [16] or visual sensing data [17] to im- prove slip detection. Recurrent Neural Networks and Graph Neural Networks are also used for slip classification [18, 19]. While [18, 19] only use tactile readings for slip detection, we use an LSTM-- based model with combined tactile data and robot actions predict slip in a prediction horizon.
Grip force controllers based on tactile readings are usually designed avoid slip [11]. [20, 4, 7] increase the normal force until the ratio of normal to shear force becomes smaller than the known/estimated static friction coefficient. This method can lead to deformation or damage of del- icate objects, e.g., soft fruits. [4] propose heuristic to limit deformations of a deformable object but it needs to measure the initial grasp width and object deformation. [21] suggest increasing the number of gripping fingers to avoid slip by using multi-finger hand. In bi-manual manipulation, [6] performs hand pose correction to bring the resultant force inside the friction cone as slip avoid- ance controller. The above approaches have certain limitations where increased grip force is not an option (e.g. (1) Franka Emika gripper is not force-controlled or (2) in delicate object manipulation).
Learning from demonstrations [22, 23] or optimisation can be used for planning robot movements. E.g., online trajectory re-planning is effectively used in areas such as agile quadrotor control [24, 25], but to the authors' knowledge, it is the first time it is being used for slip avoidance in dynamic ma- nipulation tasks. The main difficulty relates to the lack of analytical models for tactile/slip dynamics. By the proposed constrained optimisation setting, we seek to benefit from the data-driven models for slip prediction and model-based approaches for online trajectory optimisation.
3 Methodology
Our proposed slip avoidance controllers are built in two stages: first, the offline slip classification phase where the I.STM-based networks (Section 3.1) are trained on a collected dataset (Section 4) and second, the online trajectory adaptation phase where the trained models are used in constraint optimisation setting (Section 3.2) to minimise the expected value of the slip signal and deviation from reference (desired) trajectory.
3.1 Slip detection and slip prediction models
Slip classification is explored by three main approaches the literature: (i) incipient slip detection [10]. (ii) full slip detection [13, 4], and (iii) slip prediction [5]. Incipient slip detection requires precise slip calibration and sensor mechanical compliance 17, 26]. Our focus is on using data-driven approaches for full slip detection and slip prediction. Tactile shear forces have proven to be a useful
ii

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Referenceldesired trajectory
X(t) (())
Slip Prediction
arg min B(S) + Al X(w, の) Xxes
roqui
S
ap
tactie
Trajectory adaptation
Robotic
(wi, way w (()
X
Figure 1: A schematic of the proposed proactive slip control (blue block) that is rumning 30 Hz and includes a slip prediction module (grey block), and predictive controller for trajectory adaptation (orange block).
feature for slip classification [10]. This grounds the higher rate of changes for shear force when the taxels are losing contact with the objects, which is classified as an anomaly versus stuck phase. [27, 28] show the benefits of using history of tactile features and temporal processing by modules such as LSTM cells. As such we use the shear forces of the uSkin tactile sensor (introduced in Sec- tion 4) with two LSTM-basee models for slip detection and prediction problems. In slip detection, time window of length C of shear tactile forces Nt-C:t RC'x48 are used input features to classify the binary slip class G = fx, where G. E {Cutips Snon-stip! } at time step t. For slip prediction,
multi-modal LSTM-based model (inspired by [11, 28)). This model uses time windows of tactile features x; E IRC 48 and robot future actions a, 41:1-++7 R to classify the slip state C++2 time step t (where T is the prediction horizon). The architecture of the slip prediction model is shown in Fig. 1. The slip detection model has similar architecture without the robot data input and the corresponding dense layer. Fusing the latent vectors of two input modalities seeks to exploit the future robot motion for slip prediction.
3.2 Closed-loop slip control by trajectory adaptation
The studies in closed-loop control design for slip avoidance are limited to gentle short-range lifting motions [4, 20, 29]. Our proposed approach extends slip control beyond simple slow lifting motions. First, we collected data set of dynamic moving tasks by pre-defined task space trajectories, i.c. a linear motion with various velocity profiles and ranges of acceleration/decellrattoo values. We learned slips are initiated by the dynamic motions of the robot as opposed to being caused by object weight non-stable grasp for slow lifting tasks in literature [4, 20, 29]. Therefore, we propose two trajectory oppinisation/adaaaationn methods incorporating tactile and robot proprioceptive data slip control. We tested/compared PSC and RSC approaches real-time scenarios on real robot experimentation.
Reactive slip control by trajectory adaptation Inspired by episodic policy search [30]. we represent the robot actions (e.g. task space velocity) by linear Gaussian parametric models. As such, robot's action at time can be represented as X, Σソ Wo0s (t), where Wi E IR are the weight
parameters and o,s are the basis functions with Gaussian forms (t()) exp (- [t- j = 1, N is the number of basis functions.
where 20
A sequence future robot actions with length in a matrix form will become X441447 == 4T x W where (t) = [o, (t), dz (t), ..., 2, (f)] E & and W {w,, W2, ..., w, E RN the matrices of basis functions and weight values respectively. The mean values of the Gaussians spread with equal distance in the horizon length 1 : T and the parameters chosen such that Gaussians have reasonable widths and overlap.
The main policy in grip force adaptation for slip control is to incrementally increase the normal force (N (1)) until the output of the slip detection model (S) is zero [28]. This usually formalised as ((++1)= (t) + 8N, where 8N is the force increasing step. Likewise, implement RSC by

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adapting the robot movements trajectory by decreasing task space velocity by fine steps (we learned slip happens during the initial parts of movements with high acceleration) until the output of the slip detection model is zero. If no slip is detected, the robot will follow the desired reference trajectory- We empirically come up with a formulation in Eq. after testing different ones. This includes an affine combination of slip cost and reference trajectory tracking cost. Although in RSC we do not need to optimize over a receding horizon, this constrained optimisation will be later extended in PSC (in Eq. 2) allowing easy comparison between PSC and RSC.
[|X++184(WWWC Φ) - Xrrff xt1++Tll + astr (x
arg min
subject to: 1b < xx+1 X < ub
Where X is the generated trajectory by optimisation, X is the reference trajectory, S E {0, } is the output of the slip detection model introduced in 3.1, a and e E R+ are hyper-parameters, and Xeehe is robot's observed velocity at time t. The first term in the objective function aims to minimise the Euclidean distance between the generated and the reference trajectory. The second term in Eq. results in generated smaller velocities (where the object is moved with large acceleration). As such, by choosing a to be in 0 < a < and as a small positive value i.e. 0.01, for non-slip mode when S = 0. the effect of the second term vanishes (a 00 robot's task space velocity until there is no slip. The imposed constraint keeps the robot action at t bounded to avoid abrupt deceleration and ensures the admissibility condition of the robot's low- level controller is met. We selected the exponential term for the slip signal in that it resulted in more stable optimisation and better slip avoidance behaviour than other forms, e.g., linear cost of slip. The hyper-parameters are empirically chosen as a = 0.5 and e = 0.01.
()) and if S 1, it constantly reduces
1))
Proactive slip control by trajectory adaptation Predictive controllers, c.g. [31], showed success when dealing with nonlinear systems with the risk of being stuck in a local minimum of the corre- sponding utility function. They can achieve solution close to the global optimum by finite-horizon optimisation. We use the same approach by solving the constrained optimisation in prediction horizon and applying the first component of the generated action sequence at each time step.
Parametric action representation has benefits in exploration complexity versus direct search action space [30]. The benefit is more effective when the action is optimised in a future time horizon rather than single time step. In this regard, in the trajectory adaptation problem, we use linear Gaussian parametric representation [32] for the robot action sequence. This leads to fewer search parameters in the optimisation problem relative to searching in the action space. In this scenario, by the slip prediction model introduced in section 3.1, we close the loop for PSC by solving the constraint optimisation in Eq. 2. The predicted slip value time t is function robot's future trajectory, S ((X+1447(W o)).
arg min ||X++144T gref
(2)
subject to E 1 w)) 0
< X - X <
Our PSC aims at minimising the expected slip over the prediction horizon while keeping close the given reference trajectory as shown in Fig. 1. In contrast to RSC in Eq. 1, we consider the slip term as a non-linear constraint in Eq.2. This forms a more effective optimisation for slip minimisation since the optimisation library use forms Lagrangian function by optimising the Lagrange multipliers as opposed to the heuristic form of the multiplier in the second term of Eq.
Data set
4.1
Tactile sensor and manipulation task
Spatial resolution, reading frequency, and mechanical compliance are the main features of a tactile sensor reviewed in [33]. We use uSkin, magnetic-based tactile sensor from XELA ROBOTICS
iv

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shear
Space
daaben (deg) Im & (mm))
Velocity
Kalman
RealSense Camera
Tactile shear
Marker rotation
Goal point
A Aruco Marker
-000
uSkin Tactile Sensor
Tactile normal
Slip label
100
Object Rotation
(slip]
300
50
50
13g
step (60nz)
step (60hz)
(a)
(b)
Figure 2: (a) Experimental setup including Franka Robot, 4x4 uSkin tactile sensor on its fingers, box-shaped object, ArUco marker on the object, and RealSense camera. A sample of trapezoidal task space velocity trajectory is shown on the top left. (b) Data set features: tactile readings, robot trajectories, marker trajectory, and slip signal.
that works with 100 Hz frequency. It publishes 3-axis point-wise force measurements for 16 taxels arrayed in a 4x4 matrix (Fig. 2a). A pair of sensors are mounted on 3-D printed fingers for a jaw gripper. We more slip-cases, we reduce the friction coefficient by covering the uSkin sensor with a latex cover and increase the object weight with large metal bolts fixed inside the box that do not manipulation. A wrist camera reads the pose of the ArUco marker attached on the top side of the object measuring the relative motion of the object w.rf the hand. This is used to create labels for ground truth slip values. Maximum noise amplitude for marker readings is 0.38 [degrees] for rotation and 0.5 [mm] for the position in this range of distance.
box-shaped object (4x 15x25 [em] and 400 [gr]) for training and to encourage
during
The robot grasps the object from a top grasp pose with the jaw gripper, lifts it for 10 [em] (in + direction), and executes linear motion in the horizontal (XY) plane. The linear motion encourages slip events which are a direct outcome of dynamic manipulation forces rather than object static weight. As such, for trajectories with small accelerations, the object keeps its upright orientation; While high accelerations cause the object's large rotation (as a result of the object's 's centre of mass moment of inertia around the grip axis) and slip. We task space velocity controller with several reference trajectories with trapezoidal profile and various maximum velocity (VMAx) values. The velocity commands are converted controller. Fig. 2a shows a sample of the reference velocity profile (start and end point of the motion are [0.4m, 0.25m, 0.3m] and [0.1m, 0.25m, 0.3m) respectively in robot's base frame); As VMJ changes, the acceleration and deceleration values change accordingly such that the area under the curve which is equal to the overall translation remains constant.
joint torques through the robot's low-level joint impedance
4.2 Data collection and pre-processing
We collected data set of 660 linear motion tasks containing (i) tactile readings from two fingers (at 100 [Hz]), (ii) robot state data (at 1 1,000 [Hz]) including joints position, joints velocity, task space pose, and task space velocity, (ii) Marker pose data 60 [Hz]), and (iv) Slip label values based on marker's rotation (see Fig. 2b). We use ROS ApproximateTime policy to synchronise the data. We applied Kalman Filter the raw tactile readings to delete the measurement noise on the data (See Fig. 2b). Considering the linear robot motion, slip can happen either as pure rotational slip, or rotational slip followed by translational slip (which usually leads to object dropping and task failure). As such, a threshold on the rotation of the ArUco marker is used to create the slip labels. Based on marker rotations for slower ranges of motions with no slip, rotations > 6 are labeled as slip-case. The data set along with preprocessing and models will be publicly available for future investigations of cendiincond tactile or slip prediction [34, 35] or slip detection models in dynamic manipulation task.

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15
rotation (dag)
rotation (deg)
-10
-10
-10
-15
50
100
150
200
-15
0
20
40
60
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140
-15
25
50
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time steo hz)
time step (00 hz1
time step
hz)
(a)
(b)
0.0
0.0
velocity (mvs)
-0.1
Appoeen
0.2
pridiction onset
onset
0.3
-0.4
reference
-0.4
reference
60
100
120
60
80
120
140
time stop 160 ha)
time stop 160 hz)
(d)
(c)
Figure 3: Object rotation around grasp axis during following a reference trajectory (a) without slip control and with (b) RSC, and (c) PSC, where > 6 are slip events. Trajectories generated by RSC (d) and PSC (e): the blue areas show variations across 10 trails; the green bands show approximate time slip is detected (predicted) for the first time.
Experimental results and discussion
We tested the proposed trajectory adaptation methods control slip in real-time in several test cases of manipulative trajectories with the Franka Emika robot connected to PC with AMD@ Ryzen 16-core processor and 64 GB RAM running ROS Noetic. We use the ONNX library reduce the inference time of the slip classification Py Torch models from [ms] to 0.2 [ms]. Scipy optimisation
library in python (i.e. minimise() function with 'trust-constr') used to solve the constrained optimisation in Eq. 1 and 2. Each test trial is executed 10 times on the robot to achieve statistically meaningful results.
The metrics to compare the performance of slip controllers are (i) Resulted optimality value (ROV) from the optimisation library (i.e. gradient of the Lagrangian function, and indicative of the con- vergence of the optimisation), (ii) Maximum amount of object rotation (MOR) in the trial, (iii) Execution time (ET) in millisecond which shows the computation complexity, (iv) instances ro- tations larger than 6%, "RTS" (Rotation >> 6 Time Steps) score which is the number of time steps that the object had rotations larger than 6 degrees. Larger rotations and large number of cases of RTS indicate a higher probability of failure, and (v) distance to reference trajectory (DRT) which is the sum of Euclidean distance between the reference and adapted trajectories over the task execu- tion time. In total, 300 testing trials are executed with the train and test objects. Slip classification accuracy and f-score on the test (20% the data) are 98% and 0.81 for the detection and 94% and 0.74 for the prediction models, respectively. The planning horizon l is chosen by trading off between classification scores and controller success rate. While longer horizons worsen the classi- fication performance, they give the proactive controller more reaction time and increase its success rate by having a larger safety margin. By testing horizons lengths 5, 10, 15, and 20, we picked 10 as the best case. For the context frames length C, considering the sensory data frequency (60 hz) and task completion time (1.2-3 seconds), we chose C' 10 as a reasonable value including past 0.17 seconds of tactile data.
Reactive versus proactive slip control with trajectory adaptation: As the first test case, we compare RSC and PSC with the same reference trajectory and design parameters. Resulted object rotation for RSC and PSC are presented in Fig. 3b and Fig. 3c respectively; which can compared to the case of executing the reference trajectory without slip control in Fig. 3a (horizontal lines in Fig. 3a show the time instance the object is dropped off the robot's hand due to slip). RSC activates after slip is observed in reality (i.e. rotation > 6 around time step 70 in Fig. 3b), but it keeps the
vi

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# Basis
Model
ROV
MOR
ET
RTS
DRT
Functions
2
0.80 1 0.72
12.80 1 7.58
9.79 + 1.33
35 8
1.31 0.15
0.15
3
9.06 0.56
1
31
1.15
4
0.67
9.63 1.50
10.49 1.62
30:55
1.26
Reactive
5
0.63 0.31
8.82 = 0.61
9.96
28 1
1.04
6
0.47
9.09 1.19
=
32 10
1.05
7
9.66-1117
9.70
33 6
1.06
8
0.28 + 0.19
9.80 = 0.75
9.99 # 1.50
33 +: 10
+
2
0.75 0.60
3,99 0.58
11.73 5.35
010
1.67
3
0.72 0.67
6.14 + 1.52
22.57 = 1.79
4 2
1.56 1.56
4
0.51 0.41
7.16 1.07
2
5
1.49
Proactive
5
0.40 0.34
9.06 0.55
29.61 3.26
8
1.26 0.20
6
0.33 + 0.22
9.75 + 0.65
32.09 = 5.05
13 1
1.23 1 0.18
7
0.30 = 0.22
9.41 = 0.55
35.27 = 3.65
11 +
1.17 + 0.36
8
0.25 0.20
9.14 + 0.37
37.55 3.84
00:
1.21 +: 0.21
Table 1: Trajectory optimisation performance by different number of basis functions. For all of the metrics, smaller values show better performance. (all metrics are defined in Section 5)
rotation bounded in the rest of the trial. PSC (shown in Fig. 3c) outperforms RSC in the acceleration phase of the motion as the slip control activates before slip actually initiates. Both controllers show 100% success in avoiding task failure while in the mode without slip controller (Fig. 3a) 19 trials out of 20 led to object dropping and failure.
The velocity profiles generated by RSC and PSC shown in Figs. 3d and 3e. Although these fig- ures show RSC keeps closer than PSC to the reference trajectory. it yields much more rotation of the object and non-smooth (with higher jerk that is undesirable) movements. PSC yields trajectories farther from the reference trajectory but has smoother acceleration and deceleration trends which can lead to smaller slip instances. This is mostly because the weight of the slip avoidance relative to the Euclidean distance term in Eq. 2 is set by the Lagrange multipliers of the optimiser in PSC. This makes the trajectories get more distant from the reference trajectory to satisfy the slip constraint.
The effect of the number of basis functions on trajectory adaptation: We tested RSC and PSC (Reactive and Proactive, respectively, in Table 1) with number of basis functions ranging from to 8. While PSC sees a prediction of slip it has enough time to deviate enough from the reference to avoid slip more effectively. This leads having smaller RTS and larger DRT for PSC. On the other hand, RSC has a shorter reaction time relative to PSC, which leads to larger RTS and smaller DRT. In terms of improving RTS (slip prevention performance), maximum improvement in RSC by changing the number of basis functions is with 5 basis functions for both, improves slip prevention by can be higher for other numbers of basis functions.
35-28
x 100 = 20%. While switching from RSC to PSC 28-8
35
28 x 100 = 71%. This improvement
Depending on how many basis functions are used, the overlapping area of the first Gaussian with the next ones can change (since we take only the first element of the generated action sequence in the horizon, the change in the beginning part of the horizon is most important). For RSC, this change has positive effect on having smaller RTS up to 5 basis functions. For reference track- ing (DRT), increasing the number of basis functions improves tracking performance. For PSC, 2 basis functions resulted in zero RTS with the largest distance to the reference. Since the con- troller has fewer degrees of freedom in this case, the cost it needs to pay for preventing slip is to get much farther from the reference (the generated trajectories can be seen in Figure (a) in the Appendix). This is a very conservative proactive controller and based on how much weight we want to put on slip prevention w.r.t reference tracking, we could prefer this controller. number of basis functions is control parameter that we can use to specify how much conserva- tive we want to be about slip avoidance. The safety margin is achieved by reducing the number of basis functions, but the cost is getting farther from the reference trajectory. Changing the ba- sis functions from 2 to for PSC, improves tracking performance by 6%, 4%, 15% respectively, but going further to basis functions has large negative effect RTS ( 13-8 x 100 62%).
fact, the
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Object
ROV
MOR
RIS
DRT
theBest
0.51 0.41
7.16 1.07
4
1,53 + 0.2
THINS
0.54 0.43
7.88 + 1.38
8 3
1.71
Carss
0,52 + 0.39
8.02
2.12
7
1.61
RICE
0.61 + 0.44
10.58 3.56
10+
1.51 + 0.2
Monster
0.66 0.46
36.20 + 13.93
28
1.24 + 0.3
Objects
Slug killer
0.52 +
8.23 + 1.86
6 0
1.62 + 0.3
Figure 4: Train and test objects.
Table 2: Tests on novel objects.
As such we choose 5 basis functions to be the best value for PSC based on this quantitative anal- ysis. While ROV improves by an increased number of basis functions for both controllers, MOR achieved by RSC does not show clear improvement after 5 basis functions. In contrast, it leads to a larger MOR for PSC. The computation time increases proportionally in PSC, while remains roughly constant in RSC. The reason for the larger computation time for PSC is that the Jacobians of the slip constraint should be numerically estimated by the optimisation library which needs 30-50 calls of the slip detection/prediction networks in each optimisation iteration.
Generalisation tests with novel objects: To evaluate generalisation on novel tactile features we test PSC with 5 basis functions on the novel objects shown in Fig. 4 (from left to right the objects are called theBest, Slug killer, Monster, RICE, THINS, and Carrs respectively). Three of the objects have a box shape similar to the training object, and two are cylindrical for more difficult general- isation cases with novel contact geometry. Table 2 shows the resulted metrics for the train (first row) and the test objects. The first two test objects have similar geometry and weight to the train object but with different friction behaviour where the PSC performance is close to the ones with the train object in terms of both RTS and MOR. Although the RICE box has similar geometry, the PSC performance is relatively poor. This is partially related to the lighter weight and different friction be- haviour of the box cover. We observed our PSC can successfully avoid slip in manipulating objects unseen in the training.
The Monster can with dynamic weight (liquid inside), deformable surface, and different geome- try (cylindrical) and friction is the most challenging test object for the controller and resulted in the worst performance metrics values. The PSC can effectively control the Slug Killer can slip which has a plastic and rigid material very different from the train object. The results in Table demonstrate PSC can generalise to novel contact geometry for rigid object. In fact, the pro- posed constrained optimisation setting is dependent on how reliable the slip detection/predictionn model performs. As such, as far as the slip classification model can generalise novel objects, the proposed slip avoidance control is guaranteed to effectively avoid object slip and task failure.
Conclusion
We presented a novel approach to slip control by online trajectory adaptation. This is very important as the increased gripping force control policy is not an effective solution in many settings. We presented two novel approaches for slip control by robot trajectory adaptation (i) Reactive Slip Control and (ii) Proactive Slip Control. We showed the success of the slip controllers in real-time tests on a dynamic real-world manipulation task. We show Proactive Slip Control outperforms the reactive one and it can generalize to objects unseen in training. Our future works include extending our dataset to include different tactile readings, e.g. camera-based tactile readings, and more real- world manipulation experiments, i.e. different objects and manipulative movements.
Our approach is heavily relying on the slip data set to predict the slip. We have only one class of trajectories (i.c. a path with different velocity profiles) in our dataset which limits the prediction performance. We will extend the dataset in our future works and collect data with different classes of manipulative movement trajectories. The training dataset only includes one object with varying weights. Nonetheless, the slip controller could successfully generalise to unseen objects in our test cases. We will improve our dataset by adding more objects (from multiple classes) to our dataset in our future works. The controller is using one predicted slip signal to adjust its next action. Using all the time steps in prediction horizon may improve the performance. In our future works, we will use classifier for different predicted tactile readings provided by an action-conditioned tactile predictive model to improve the performance of slip control.
Limitations:
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Acknowledgments
This work was partially supported by Centre for Doctoral Training, United Kingdom (CDT) in Agri-Food Robotics (AgriFoRwArdS) Grant reference: EP/S023917/1; Lincoln Agri-Roboties (LAR) funded by Research England; and by ARTEMIS project funded by Cancer Research UK C24524/A300038.
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Reviews:
[{"review_id": "amQE3iWY9FK", "reviewer": "Reviewer_b9H8", "summary": "The paper develops a novel algorithm for contact slip avoidance in robotic manipulation of objects. The algorithms consists of an offline procedure of learning a slid detection and prediction model and online trajectory adaptation to prevent slip. Several experiments are conducted. \n\n", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n- The paper deals with an interesting and relevant problem. \n- The proposed method seems to be novel with respect to the related literature. \n- There are numerous experimental results to test the method.\n\nWeaknesses: \n- In some parts, the paper lacks clarity and good structure. The most important is that the overall algorithm is unclear from a first readthrough.  I understand that two models are trained using offline trajectories, one for slid detection and one for slip prediction. Then these models are used in the online optimization process. However, this is not explicitly mentioned in the paper.  \n- Since there are not dynamics taken into account and no theoretical guarantees, the proposed algorithm should be tested with other state-of-the-art algorithms on slip prevention. \n\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "The paper seems to propose a novel algorithm, but it needs further revision on the structure and presentation as well as the experimental results.", "technical_quality": "Good: Generally convincing, but some additional experiments or analyses needed.", "clarity_of_presentation": "Fair: Major revision needed to improve organization or clarity.", "potential_impact": "", "robotics_focus": "Sufficient demonstration on hardware", "quality_of_limitations": "Limitations are addressed clearly", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "3: The work is incremental and unlikely to have much impact, even though it may be technically correct and well executed.", "reviewer_expertise": "3: The reviewer is fairly confident that the evaluation is correct"}, "extra_text": {"issues": "The issues to be addressed consist of:\n-  Lack of good flow and structure, I believe a section before Section 3. summarizing the flow of the proposed algorithm, breaking down the offline learning and online optimization would be very beneficial.\n- Lack of comparative experiments with respect to related works. \n\nFurther, why is the robot's action parameterized by a Gaussian parametric model? Why not just consider the robot action as the optimization variable? Similarly, the notation $\\dot{X}$ (without subscripts) seems to be undefined. \n\nThe paper should be revised with respect to some grammatical mistakes. "}}, {"review_id": "CgljTCVXV4", "reviewer": "Reviewer_PezY", "summary": "The goal of this work is to develop a slip control mechanism for robot manipulation tasks where objects being manipulated may move relative to the gripper due to insufficient shear force. The key idea is to learn a slip prediction model that takes tactile sensor data and planned future actions as input and outputs an estimation of whether the object is going to slip. The learned slip prediction model is then used as a constraint in a trajectory optimization formulation to adjust the planned robot motion in order to proactively prevent slipping. The proposed model is trained with a single object with varying weights and robot motions and evaluated on novel objects. It is also compared to a baseline method of reactive slip control, where the robot motion is adapted after detecting the slip, and the robot motion without considering the slip. The proposed method achieved a comparable success rate compared to reactive slip control, while being able to reduce the object orientation.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths\n- The idea of learning a predictive model for preventing slipping during manipulation is interesting and the proposed algorithm seems concrete.\n- The collected dataset for slipping prediction/detection could be of interest to researchers in relevant fields.\n- The result is demonstrated on real, physical robots with non-trivial manipulation tasks.\n\nWeaknesses\n- The advantage over reactive slip control doesn’t seem a lot\n- The performance of the proposed method (PSC) seems to get worse as the number of basis functions increases. This raises a concern for the ability of the algorithm to be applied to more complex tasks where a higher-order motion is needed.\n- The designed customized optimality is not very straightforward. I think it’s more clear and simpler if they separately report the slipping events and the tracking performance, instead of merging them with some weights alpha.\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "As mentioned in the weaknesses above, my main concerns for the work is that it didn't show much advantage over the reactive slip control, which could also use some additional tweaking, and it's not clear how well it can be extended to more complex tasks given the experiment results. That being said, I think the idea of learning to proactively prevent slipping during manipulation is interesting, the dataset generated and the designed task in this work may inspire future research in this area. Thus I'm currently leaning towards accept.", "technical_quality": "Good: Generally convincing, but some additional experiments or analyses needed.", "clarity_of_presentation": "Very Good:  Well organized and clear.", "potential_impact": "", "robotics_focus": "Sufficient demonstration on hardware", "quality_of_limitations": "Limitations are addressed clearly", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "3: The work is incremental and unlikely to have much impact, even though it may be technically correct and well executed.", "reviewer_expertise": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"}, "extra_text": {"issues": "- What's the range of weights seen during data collection? It is mentioned that metal pieces are added to the box but I didn't find how many of them.\n- How would the reactive slip control perform is a buffer is used, e.g. if slip is defined as exceed 6 degrees in orientation, the detection model can be trained to trigger when the orientation exceeds 4 degrees. The paper mentioned that RSC can already track the reference motion better, if setting the threshold more conservatively also improves orientation control it may perform at least as good as the proactive one?\n- What are the statistics for the two terms in the customized optimality separately? It is mentioned that RSC tracks the motion better, which corresponds to the first term. Does it mean that slip happens more often in RSC?"}}, {"review_id": "8XWkn0-YyoH", "reviewer": "Reviewer_xX6y", "summary": "This paper applied machine learning to the problem of slip detection and avoidance in manipulation tasks. They implement two solutions: one a purely reactive method which uses an LSTM to detect slip events triggering the controller to slow it's velocity. The second implementation is also an LSTM but takes in both past data from the tactile sensor and future planned actions from the controller to predict slip. These future predictions are then used to re-optimize the planned trajectory to pre-emptively avoid slip. ", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n- Good application of learning. Slip is very difficult to model and learning methods are becoming more common for this purpose.\n- Well justified choices in network and problem formulation\n\nWeaknesses:\n- The paper is unclear on some important information such as experimental setup.\n- I generally find the paper is written in a confusing way with implementation details in the beginning and theory later resulting in a lot of back and forth reading.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "I think this paper is a solid idea with good implementation. The weakest parts are in the writing of the paper itself which I expect the authors can improve. I have listed my issues with the paper in it's current form below.", "technical_quality": "Good: Generally convincing, but some additional experiments or analyses needed.", "clarity_of_presentation": "Fair: Major revision needed to improve organization or clarity.", "potential_impact": "", "robotics_focus": "Sufficient demonstration on hardware", "quality_of_limitations": "Limitations are addressed clearly", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "3: The work is incremental and unlikely to have much impact, even though it may be technically correct and well executed.", "reviewer_expertise": "3: The reviewer is fairly confident that the evaluation is correct"}, "extra_text": {"issues": "- The biggest issue with this paper is that the experimental setup and reference trajectory is not explained. It was not clear at all why rotation was used to estimate slip until watching the video. The authors should clearly explain the goal trajectory used in the experiments and how exactly does the object slip out of the robot's grasp. From looking at Figure 1, I assumed the robot just lifted the box in the air which made the entire paper very confusing.\n- The text in Figure 1 is too small and blurry. Also adding labels for all the signals would make it more clear. For example writing \"actions\" and \"tactile data\" next to the arrows coming from the robot.\n- I find the order of the paper confusing, perhaps the datasets section should go after Methodology.\n- Figure 3 should include the object names either in the figure or caption so readers can know which object is theBest vs THINS for example.\n- Some important implementation details are missing such as planning horizon and tactile data window length."}}, {"review_id": "-HgmGWepxyO", "reviewer": "Reviewer_FJ4L", "summary": "This paper proposes reducing slip during dynamic motions by adapting the trajectory, specifically decreasing the velocity. This is in contrast to previous papers which normally focus on moderating gripping force. After gathering a dataset of slip experienced while completing linear motions with varying acceleration and deceleration profiles, the paper proposes two LSTM-based models: slip detection and slip prediction. These models are used in the two proposed methods: Reactive Slip Control (RSC) and Proactive Slip Control (PSC). RSC reactively decreases the task space velocity until slip detection gives zero while PSC formulates a trajectory optimization problem where the objective is to minimize expected slip while staying close to reference trajectory. Unsurprisingly PSC outperforms RSC, since it can plan to prevent slip before it occurs. Since trajectories are modeled by linear gaussian parametric models the paper compares how the number of basis functions impacts performance. ", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "This paper takes an interesting approach to reducing slip by modifying the trajectory rather than the gripping force. The paper argues that in several scenarios, such as manipulating delicate objects, it may be undesirable to simply grip harder. The paper offers both a reactive and proactive formulation to address slip. While the results show that a proactive approach leads to less overall slip, it would be interesting to discuss if there is ever a case where RSC would be preferable to PSC. There is a very interesting discussion in the appendix on when grip force and trajectory adaption might be necessary in combination. \n\nBoth RSC and PSC are well-explained. The video also does an excellent job of communicating the main points of the paper and showing off the results. \n\nOne slight weakness of the paper is that the training set is composed of only one object (with varying weights) and the only trajectories are linear motions (with varying acceleration and deceleration profiles). As shown in the experimental section, this limits the generalizability. In testing on novel objects, the maximum rotation for all objects is above the desired 6 degree threshold. A larger training set could tease out where the generalization issue stems from. \n\nIn considering slip, the paper ignores translational slip, only focusing on rotation slip (defined as a slip of more than 6 degrees). Is translation slip considered less of an issue? Would translation slip be a larger concern for a different class of motions? The presentation of results is more clear when focusing on just rotational slip. However, given the emphasis on dynamic motions, it is a limitation to ignore translational slip. ", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "I am weakly recommended that the paper be accepted. The paper presents an interesting alternative to addressing slip and distinguishes itself by considering dynamic trajectories. The two proposed methods (RSC and PSC) are mathematically sound and there are interesting and relevant robotics experiments comparing them and analyzing their parameters. While the proposed methods could be more general, by leveraging a more diverse training set and considering translational slip, I believe the contributions are still compelling and well-demonstrated through the experimental section.  ", "technical_quality": "Very Good:  Convincing.", "clarity_of_presentation": "Very Good:  Well organized and clear.", "potential_impact": "", "robotics_focus": "Sufficient demonstration on hardware", "quality_of_limitations": "Limitations are addressed clearly", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "4: The work contains interesting new ideas that will potentially have major impact in robotics or machine learning.", "reviewer_expertise": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"}, "extra_text": {"issues": "It could strengthen the paper to discuss how this method could be applied for translational slip and how the system could handle both rotational and translational slip. If possible, it would be great to consider a larger set of training objects/trajectories, although it is not clear that this is feasible in the rebuttal period.\n\nA few more minor notes, questions, comments:\n- In Fig. 2 it would be easier to compare the rotations if all the y-axes were the same. Additionally, is it correct to assume that the red line in (d) and (e) corresponds to the reference trajectory?\n- How is the time window C chosen for RSC and PSC? How would varying this window impact the results? Likewise, how was the prediction horizon h chosen? (For the experiments it seems h=10). How would varying this impact results?\n- The explanation of RSC mentions that formulation was found empirically. Is this referring to the form of the cost function or the constants? What other formulations were considered and why was the presented form selected?\n- Is there any intuition on why PSC performs better with a small (2) set of basis functions? In particular, are there thoughts on why RSC needs more (5) basis functions?\n- Sec 3 mentions that uSkin can be challenging to calibrate. Given that this was mentioned, did this impact the results in any way and are there tricks that can be done to mitigate this issue?\n- With respect to the training set object, it would be helpful to mention, perhaps in the appendix, what weights were used. Is it also correct to assume that the metal pieces were fixed in the box such that they could not move?\n- It might be more beneficial to place the data set section after the methodology section, since doing so would provide more context for the data set section.\n- On the first page, line 34 there is a missing close paren."}}]

Author rebuttal:

Decision: Accept (poster)