Tactile sensing provides essential contact information for robotic manipulation, yet incorporating it into pretrained vision-language-action (VLA) models remains challenging. A common concern is that simply introducing touch during task-specific fine-tuning may fail to bridge the cross-modal gap, yielding limited gains or even reduced success. Consequently, existing methods often rely on large-scale tactile policy pretraining or separate visuotactile alignment, adding data requirements and training stages. We introduce SimpleTouch, a simple VLA extension that augments π0.5 with a tactile expert, to test whether these additional stages are necessary. Leveraging all tokens from a frozen pretrained tactile encoder, the expert learns from action supervision and multi-horizon prediction of future tactile latents. This single-stage training uses only task demonstrations, without additional tactile policy pretraining or separate alignment. With 50 demonstrations per task, SimpleTouch achieves the highest success rate among evaluated methods on all six UniVTAC tasks. Its average success rate reaches 77.5%, compared with 45.2% for FTP-π0.5 and 66.7% for FTP-1, corresponding to gains of 32.3 and 10.8 percentage points, respectively. Across four real-world tasks, it averages 71.3%, exceeding FTP-1 by 8.8 percentage points. These results demonstrate that, given pretrained VLA and tactile representations, additional tactile policy pretraining is not a prerequisite for strong performance on these tasks, offering a simpler route to contact-rich manipulation. Project page: https://simpletouch-robot.github.io/
Figures & tables
Figure 1: Overview of \simpletouchfont SimpleTouch. \simpletouchfont SimpleTouch combines an off-the-shelf pretrained tactile encoder with a tactile expert to learn from 50 task-specific demonstrations without tactile policy pretraining. The chart on the right reports success rates on six UniVTAC and four real-robot tasks.
Figure 2: \simpletouchfont SimpleTouch architecture. A tactile expert uses complete current tactile features from a frozen encoder and learns multi-horizon future prediction. In the right-side training and inference masks, rows are queries, columns are keys/values, and colored cells mark permitted attention. The symbols c0 , z0 , z1:H , and a1:H denote the current visual-language and state context, current tactile tokens, future tactile tokens, and action tokens, respectively, following the token order shown above.
Figure 3: Real-world setup and rollouts. The left panel shows the PiPER platform and sensing hardware; the right panel shows all four tasks—Play Mahjong, Wipe Board, Pick Up Chips, and Insert USB—with time progressing from left to right throughout each rollout.
Method
Lift Bottle
Pull-out Key
Lift Can
Put Bottle
Insert Hole
Insert Tube
Avg.
Avg. w/o Lifts
ACT
42
28
20
28
19
45
30.3
30.0
π0.5
92
36
59
31
26
34
46.3
31.8
VITaL
72
47
8
32
25
34
36.3
34.5
UniVTAC-ACT
71
46
29
31
24
56
42.8
39.3
Tactile-VLA
97
32
15
10
41
56
41.8
34.8
FTP- π0.5
77
30
26
19
47
72
45.2
42.0
Table 1: Results on the UniVTAC simulation benchmark. We report success rates (%). “Avg. w/o Lifts” excludes Lift Bottle and Lift Can. We highlight the best and second-best results.
Method
Play Mahjong
Wipe Board
Pick Up Chips
Insert USB
Avg.
ACT
45
15
30
0
22.5
π0.5
40
45
55
10
37.5
UniVTAC-ACT
70
35
25
0
32.5
FTP-1
95
65
60
30
62.5
\simpletouchfont SimpleTouch
100
85
70
30
71.3
Table 2: Results on four real-world contact-rich manipulation tasks. We report success rates (%). We highlight the best and second-best results.
Figure 4: (Left) Effect of the number of retained current tactile tokens per sensor; fewer tokens imply stronger spatial pooling, 0 is vision-only, 1 retains only the T3 CLS token, and 197 is the full T3 sequence. (Right) Effects of tactile-encoder variants across tasks; VAE uses a frozen visual VAE, while Trainable T3 fine-tunes T3.
Variant
Pull-out
Put
Insert
Insert
Avg.
Key
Bottle
Hole
Tube
Vision-only
36
31
25
64
39.0
w/o Tac. Pred.
60
37
83
95
68.8
Single-horizon
53
44
79
98
68.5
\simpletouchfont SimpleTouch
62
50
85
100
74.3
Table 3: Future tactile prediction ablations on UniVTAC. We highlight the best and second-best values.
Figure 6: Data efficiency on UniVTAC. Success rates of FTP- π0.5 and \simpletouchfont SimpleTouch on Put Bottle and Insert Hole across demonstration budgets.
Design choice
FTP- π0.5
\simpletouchfont SimpleTouch
Tactile encoder
T3
T3
Encoder update
Fine-tuned
Frozen
Current tactile tokens
CLS only
All tokens
Tactile expert
Yes
Yes
Future tactile prediction
No
Multi-horizon
Table 4: Tactile design choices in FTP- π0.5 and \simpletouchfont SimpleTouch; shaded rows mark design choices that differ between the two methods.
Figure 7: Training and inference efficiency of Joint, IDM, and Fast. The left panel shows success rate versus inference latency; dashed lines connect formulations evaluated on the same task. The right panels show mean training time (top) and mean task-wise success-rate/latency ratio (bottom).
Appendix figures & tables12 assets
Supplementary material from the paper’s appendix.
Appendix
Configuration
Value
Vision-language-action backbone
Base policy
π0.5
Visual-language backbone
PaliGemma ( Beyer et al., 2024 ) : SigLIP ( Zhai et al., 2023 ) vision encoder and Gemma 2B language model
Shared 8-dimensional interface: UniVTAC uses seven arm joints and one gripper coordinate; PiPER uses six arm joints, one zero dummy joint, and one gripper coordinate. States are absolute; action-joint dimensions are relative and the gripper action is absolute. Both are zero-padded to 32 internal model dimensions
Action chunk length
50
Appendix
Table 5: Model, training, and inference configurations for \simpletouchfont SimpleTouch.
Task
Demonstrations
RGB views
Predicted
Executed
Eval. trials
Lift Bottle
50
Head
50
50
100
Pull-out Key
50
Head
50
50
100
Lift Can
50
Head + wrist
50
50
100
Put Bottle
50
Head
50
50
100
Insert Hole
50
Head
50
25
100
Insert Tube
50
Head + wrist
50
25
100
Appendix
Table 6: UniVTAC task protocol for \simpletouchfont SimpleTouch. “Predicted” and “Executed” denote the action-prediction and execution horizons, respectively.
Figure 8: UniVTAC simulation task rollouts. Each row corresponds to one of the six evaluated tasks and shows five key frames illustrating its manipulation and contact progression ( Chen et al., 2026a ; Yuan et al., 2026a ) .
Figure 9: Hardware used for real-robot experiments. (a) PiPER six-degree-of-freedom arm. (b) Pika parallel gripper and teleoperation interface. (c) GelSight Mini tactile sensor; two units are mounted on opposing gripper fingers. Product images are from the respective manufacturers.
Figure 10: Two-part 3D-printed mount for the GelSight Mini. (a) Main bracket and (b) companion base; the columns show front, side, top, and perspective views. Both parts form one gripper-mounted sensor assembly.
Task
Demonstrations
RGB view
Predicted
Executed
Eval. trials
Play Mahjong
50
Fixed
50
50
20
Wipe Board
50
Wrist
50
50
20
Pick Up Chips
50
Fixed
50
25
20
Insert USB
50
Fixed
50
50
20
Appendix
Table 7: Real-robot execution protocol for \simpletouchfont SimpleTouch. “Predicted” and “Executed” denote the action-prediction and execution horizons, respectively.
Task
RGB view
Initial-condition variation
Binary success criterion
Play Mahjong
Fixed
The grasped tile is either a bird tile or a one-dot tile; its pose within the gripper is varied. A two-second tactile observation is recorded at the start.
The bird tile is discarded to the table center, or the one-dot tile is placed in front of the robot, followed by a stable release.
Wipe Board
Wrist
Board pose and angle, writing size, and eraser placement in the gripper are varied.
The marked region is wiped clean while the eraser remains securely grasped.
Pick Up Chips
Fixed
The chip and supporting bottle-cap positions are varied.
The chip is grasped and lifted clear without being dropped or visibly broken.
Insert USB
Fixed
USB placement in the gripper is varied while its insertion orientation is kept consistent with the port.
The connector is fully seated in the port and remains inserted after release.
Appendix
Table 8: Real-robot task protocols. Each policy is trained with 50 demonstrations and evaluated for 20 rollouts per task.
Variant
Controlled change
Exact setting
Vision-only
Remove touch from the policy
Disable the tactile encoder and tactile expert while retaining the task data and π0.5 action backbone.
Token count
Vary current-touch spatial resolution
Use the T3 CLS token alone (1), or CLS plus adaptively pooled 3×3 (10), 6×6 (37), or 8×8 (65) spatial tokens; 197 retains CLS plus the complete 14×14 grid. Counts are per sensor.
VAE
Replace the tactile-specific representation
Use a frozen Wan2.2 video VAE from Fast-WAM. The two RGB tactile images are concatenated, encoded as one latent frame, and rearranged into 98 tokens of width 192 before projection to the tactile expert. No CLS token or spatial pooling is used.
Trainable T3
Update the tactile encoder
Optimize T3 jointly with the policy from current tactile images; keep future-prediction targets fixed to cached features from the original pretrained T3.
w/o Tac. Pred.
Remove predictive supervision
Set the future-tactile loss weight to zero while retaining the full current-touch pathway and tactile expert.
Single-horizon
Use one future prediction target
Predict CLS plus the same pooled 6×6 spatial grid (37 tokens per sensor) only at t+50 , rather than at ten offsets t+5,…,t+50 .
Appendix
Table 9: Definitions of the reported representation and prediction ablations.
Mode
Training-time action conditioning
Inference
Joint
Action tokens read the K/V cache produced by the same noisy future-tactile branch. Action and tactile flow times are sampled independently, and future-tactile queries never read action tokens.
Future touch and actions are updated together for 10 Euler steps.
IDM
Actions read a separate teacher-forced future-tactile branch. Each sample uses the clean target or, with probability 0.5, an independently noised target; this branch is separate from the auxiliary future-loss branch.
Future touch is denoised for 10 steps, cached at its clean endpoint, and then conditions 10 action-denoising steps.
Fast
The action branch reads only visual-language, state, and current-tactile context. Future tactile prediction is an auxiliary training objective and cannot condition action tokens.
The cached current context directly conditions 10 action-denoising steps; no future tactile latent is generated.
Appendix
Table 10: Training and inference protocols for the three tactile-prediction formulations.
Figure 11: Additional multi-horizon future-tactile predictions for Lift Bottle, Pull-out Key, Lift Can, and Insert Tube. For each task, the current tactile image and its T3 representations appear on the left; subsequent columns compare the observed tactile image, predicted pooled latent, and ground-truth pooled latent at 5-step offsets through t+50 .
Execution
Lift Bottle
Pull-out Key
Lift Can
Put Bottle
Insert Hole
Insert Tube
Avg.
H50
100
62
68
50
58
88
71.0
H25
100
52
42
39
85
100
69.7
Appendix
Table 11: Effect of execution horizon. Every policy is trained with a 50-action prediction horizon; H25 and H50 execute 25 and 50 actions, respectively, before the next observation. Success rates are reported in percent.
Task
Formulation
Training (s/step)
Latency (s/query)
SR (%)
SR/latency (%/s)
Pull-out Key
Joint
1.891
0.946
56
59.2
IDM
2.275
1.642
51
31.1
Fast
1.862
0.614
62
101.0
Put Bottle
Joint
1.890
1.058
46
43.5
IDM
2.279
1.444
38
26.3
Fast
1.856
0.632
50
79.1
Appendix
Table 12: Task-wise training and inference efficiency for Joint, IDM, and Fast.
Vision-language-action (VLA) models have demonstrated strong capabilities in robotic manipulation, yet their reliance on visual perception limits robustness in contact-rich environments, where critical physical interaction states may not be visually observable. Existing tactile-enhanced VLA methods improve physical grounding using observed tactile feedback, but most remain largely reactive rather than explicitly modeling how contact may evolve. Therefore, we propose ForeTac-VLA, a forecasting-based tactile-vision-language fusion model that predicts future tactile states to guide action generation. Specifically, ForeTac-VLA encodes recent tactile observations into temporal representations and integrates them with vision-language features through bidirectional cross-attention. Further, a transformer-based forecasting module predicts multi-step future tactile states, enabling the model to reason jointly over observed and anticipated contact. Finally, the fused multimodal representations and predicted future tactile states are fed into the VLA backbone to condition action generation. To stabilize training, a ground-truth-to-prediction curriculum is employed when early forecasts are unreliable. Across four real-world contact-rich manipulation tasks, ForeTac-VLA achieves an average success rate of 95%, outperforming the fine-tuned VLA model by 36.25 percentage points and state-of-the-art tactile-enhanced VLA baselines by over 22 percentage points. ForeTac-VLA also maintains strong performance under low-illumination and visually cluttered conditions. Video demonstrations can be found on https://foretac-vla.github.io/
Zhengyu Tao, Xin Li, Xin Wang
Texas A&M University, College Station, TX 77843, USA
We propose TacFiLM, a lightweight modality-fusion approach that integrates visual-tactile signals into vision-language-action (VLA) models. While advances in VLAs have introduced robot policies that are both generalizable and semantically grounded, these models mainly rely on vision-based perception. Vision alone, however, cannot capture the complex interaction dynamics that occur during contact-rich manipulation, including contact forces, surface friction, compliance, and shear. While recent attempts to integrate tactile signals into VLA models often increase complexity through token concatenation or large-scale pretraining, the heavy computational demands of behaviour models necessitate lightweight fusion strategies. To address these challenges, TacFiLM outlines a post-training finetuning approach that conditions intermediate visual features on pretrained tactile representations using feature-wise linear modulation (FiLM). Experimental results on insertion and drawer opening tasks demonstrate consistent improvements in success rate, direct task performance, completion time, and force stability across both in-distribution and out-of-distribution tasks. Together, these results support our method as an effective approach to integrating tactile signals into VLA models, improving contact-rich manipulation behaviours. Project page: https://charliem7.github.io/projects/TacFilm/
Charlotte Morissette, Amin Abyaneh, Wei-Di Chang +5
McGill University, CAN · Mila - Québec AI Institute, CAN · NVIDIA, USA
Vision-Language-Action (VLA) models demonstrate impressive reasoning over visual, semantic, and spatial task variations by leveraging large-scale vision and language pre-training. They remain, however, largely blind to contact forces, which seldom manifest clearly in visual feedback but are central to contact-rich manipulation. Tactile sensing measures these forces directly, but integrating it into VLAs is difficult: tactile data is absent from the large-scale corpora used to pre-train VLAs, so adding it as a new input modality induces a distribution shift that erodes the very pre-training that makes VLAs effective. We propose Tactile Annotation Prompting for Vision-Language-Action models (TAP-VLA), a simple framework that supplies tactile feedback through visual augmentation rather than architectural change. TAP-VLA extracts shear fields from visuo-tactile sensors and overlays them as spatially-grounded vectors onto the multi-view RGB images the policy already consumes, yielding a clear, interpretable tactile cue in the VLA's native observation space. Because the architecture is untouched, the approach requires no tactile pre-training, adds negligible compute, and stays close to the pre-training distribution. Across four contact-rich tasks, TAP-VLA succeeds on 78% of trials, compared to under 50% for vision-only fine-tuning and alternative tactile-fusion baselines -- including tasks where the baselines perform no better than chance.
Mark Van der Merwe, Mohamad Louai Shehab, Jayjun Lee +4
Robotics Department, University of Michigan · Computer Science and Engineering Department, University of Michigan